What to Consider When Hiring a Data Science Team

Organizations do not face identical challenges when using data to gain insights to better run their operations. In previous blogs, we have identified possible challenges that organizations face and professionals that can be hired to help overcome these hurdles. We also discussed the roles that those professionals can play in the organization. 

When looking to hire a data science team, it is important that all hiring decisions be based on the need to solve practical problems. In this post, we will shift attention to discussing factors that need to be considered when looking to hire a data science team. 

In this article, we will discuss what to consider when hiring a data engineer, a data analyst, a business intelligence developer, and a data scientist. Keep in mind that for a successful data-driven organization emphasis must be placed on developing capable teams rather than individuals. A variety of background and experiences bring improved efficiency to the team. Interaction and learning from each other should be promoted within the team to interpret data well and develop the best recommendations. dc Analyst can help you build a team that fits your goals and can help you achieve your vision. 

Data Engineer

Data engineers are also referred to as data architects or ETL developers. Their main role is to import different data sources into a single repository. The data engineer is responsible for organizing data that will be relied on by the data science team. When hiring a data engineer, there are specific interpersonal, technical, and work experience qualities you need to consider. 

Team Collaboration

The data engineer should be able to work with other team members without unnecessary competition. 

Communication Skills 

The data engineer will need to identify data that can meet needs of decision makers and understand business rules that need to be applied to the data. This information will be received from business leaders and IT staff. A data engineer needs to be adept at interviewing people to gather the necessary information to make projects efficient.

Real World Experience 

The data engineer needs evident knowledge and work experience of data extraction, transformation, and loading. Knowledge of a popular data ETL tool coupled with a technical certification is essential with when hiring a data scientist. 

Professional Knowledge

Work experience and a technical certification in relational databases are essential. Every organization is different. Determine the best relational database platform for your organization to decide on which relational database and ETL tool knowledge is required for your engineer. 

Basic Engineer Qualifications

Knowledge and a technical certification of Hadoop and NoSQL databases are essential. Within the Hadoop ecosystem, it is important to ensure the data engineer is well versed in data movement tools. 

Business Intelligence (BI) Developer

The BI developer is tasked with identifying reporting needs of decision makers. The person in this role is uniquely qualified to translate reports and dashboards to enable generation of reports without IT assistance. We refer to this as self-service reporting. When hiring a BI developer you need to look for the following:

Team Work

Proficient in Business Operations

Analytical Thinking

The BI developer will work with decision makers in identifying their reporting needs. The BI developer should have a good understanding of how analytics is used in decision making.

Good Communication

The BI developer needs good interviewing skills to enable gathering of reporting needs

Data Visualization

The BI developer should be able to design reports and dashboards that effectively communicate data to the entire team. 

Working Knowledge of SQL

The BI developer needs a good understanding of SQL to create queries to provide required reports.

Knowledge, working experience, and a technical certification of a BI tool is essential. Commercial and open source tools are available. Thus, you must determine what is best for your organization.

Data Analyst

A data analyst is responsible for statistical analysis of data. When hiring one you need to look for the following:

Training

Training in quantitative techniques at the appropriate level is essential. Depending on the organization training could be required at the bachelor, masters or Ph.D. level. 

Communication

The data analyst will be communicating technical information to non-technical people so they should be able to present information in a simple way. They may also need to train others and write reports.  Good speaking and writing skills are therefore essential. 

Proficient in Business Operation

The analysts should have a basic and advanced data analysis skills depending on your organization’s needs. Knowledge of statistical software such as IBM SPSS, SAS, R, Stata, and Minitab among others. Your organization needs to identify which statistical tool will meet its needs. 

Data Scientist

A data scientist can apply advanced tools and techniques to understand patterns that exist in data. When hiring a data scientist you need to look for the following:

Excellent Communication 

Data science is a very technical area, so a data scientist should be able to communicate technical results to non-technical business people. Communication skills are critical because data scientists work in collaboration with business people in identifying problems. A clear understanding of the business problem and how data can be used is important.

Creative 

A data scientist needs to be creative in identifying data to be used and in handling data inadequacies.

Good Computer Programming Skills 

Skills in data science languages such as R and Python are essential. A deep understanding is not necessary, but the data scientist should be able to solve data science tasks.

Adequate Quantitative Skills

A strong background in statistics and machine learning is essential. The data scientist should be able to correctly identify and use models in problem-solving.

Professional Knowledge

Working knowledge of database design and SQL queries is important. This will enable the data scientist to acquire relevant data for their analysis. A basic understanding of Hadoop tools for big data analysis is especially important. 

To identify people with relevant skills organizations need to use multiple interviewing approaches. It is easy to identify technical skills with practical sessions but other skills such as communication and creativity may be challenging to find. Use of hypothetical situations can be used to gauge how a candidate would handle a practical situation. A portfolio of their previously completed project should also be factored in when hiring. 

The dc Analyst team is always ready to help you build a data science team that makes sense for your organization. Contact us to get started!

6 Indicators You Need a Data Science Team

In our digital economy every organization generates a sizable amount of data. There is real value in understanding and acting on insights and solutions that lay within this data. To be successful at gathering insights from data an organization needs a team of experts with various skill sets to complement each other and work collectively towards a common objective of getting value from the organization's data.

All organizations are not equal. The volume and variety of data differs, therefore, each organization has its unique challenges. The types of challenges faced dictate the type of experts that you need to consider bringing on board. 

If you find that your organization is facing these challenges you may need to hire a dc Analyst data science team to help simplify your needs:

• Receiving multiple data using various sources and team members

• Your IT or supervisory team is creating company performance reports

• Your marketing and sales teams is in need of statistical analysis for campaigns

• Your company is struggling to wrangle and organize your ever growing database

In this article we will discuss the different challenges organizations face and data analysts experts that often help organizations overcome those challenges. 

Multiple Data Sources

At the most basic level data analysis is done using spreadsheets and various reports provided by varying team members. This approach has several shortcomings. First there is no standardized way of importing the data and applying necessary transformations on the data according to the organization’s business rules and objectives. 

With every person doing data analysis on areas they feel is important the key performance indicators are difficult to identify. Secondly, due to the first shortcoming different people doing analysis on the same business processes are likely to arrive at different conclusions. This confusion wastes valuable time as it is lost investigating where differences come from instead of using data to collectively improve business operations. Thirdly, creation of multiple copies of data from various sources is to reconcile in the process of investigating and wrangling data. 

When such problems arise within an organization it is time to bring in an data analyst expert who is skilled at integrating multiple data sources into a single repository using business rules. The single repository then becomes the common data source that is relied upon for information across the organization for data analysis and reporting. 

Data analyst with the ability to gather, organize, and present data are often referred as data architects, data engineers, or ETL developers. These experts have an important role of ensuring data quality and consistency. 

Relying on IT to Create Reports

When your organization constantly relies on your IT team to create business reports an unacceptable load is placed on the IT team. Valuable time is also lost waiting for reports to be gathered and presented. IT teams have a distinct role within your organization that involves the maintenance and planning for your technology needs. 

When reports are created by your IT team they may fall short of what is required by your business team. To avoid a lack of information consider asking a business intelligence (BI) developer to handle some of your data processing needs. 

A BI developer acts as a liaison between your business team and your reporting needs. They are uniquely experienced in helping you understand their reporting needs. BI developers create reports and dashboards that can be used by your business team to meet their needs without relying on IT. The reports can also be scheduled to run at specified intervals of time and automatically sent to those who need them. This is referred to as self-service reporting.

Need for Statistical Data Analysis

Marketing. If your organization needs statistical analysis on market research data, experimental data, or data stored in a warehouse a data analyst should join your team. Data analysts help design surveys and systems that can help you understand your customers. Information data analysts can draw inferences from data to help you understand your customer preferences and buying habits. They also prepare reports that effectively communicate results of statistical analyses in simple and easy-to-understand presentations. 

Manufacturing. Data analysts support engineers and scientists with information they gain from their investigations. They interpret data to enable scientific and manufacturing efforts. For example, a data analyst will help an engineer design an experiment to identify optimal manufacturing conditions. Another example is a data analyst partnering with a medical investigator to conduct a clinical trial of a new drug and obtaining market approval. 

In addition, data analyst help organizations implement data driven quality improvement programs like 6 sigma. Armed with such information your business is able to optimize business processes. In many cases, data analysts can also train team members on how to analyze and interpret data. 

Unable to Cope with Data Growth

In every organization there are data growth projections and measures devised to cope with growth in data volume. When the systems in place can no longer handle new data volumes it is time to bring in experts skilled in application of big data technologies. Signs of inability to handle growth in data volumes include reports taking too long to run, spending a lot of time tuning queries, and trying to split analytical databases. 

When existing systems cannot handle new types of data it is important to implement an alternative system to ensure your data is accurate and usable. Data analysts are able to leverage technologies such as Hadoop and NoSQL databases to ensure analytical operations continue. 

Predictive Analytics Are Required

If your organization realizes the need for deeper analytics beyond reporting than bringing in a data science expert is the recommended next step. A data scientist is able to pose the right questions that have business value, use data to get answers, and effectively communicate to decision makers.

In many cases organizations can use predictive insights to capture relationships that exist within their data. Examples of such needs include: predicting buying behavior from demographic data and purchase history, segmenting customers into different groups, and recommending products based on the findings. Data scientists apply predictive models on the data infrastructure created by a data engineers to gain insight from the data and communicate such insights to decision makers. 

Integrating Analytics with Products

If your organization needs analytic insights to be integrated into a product then your software developer who will work closely with a data scientist. For example, a data scientist develops a predictive model that recommends products that were bought by similar customers. The data scientist and the software developer will work closely to sure the recommendation engine is properly implemented in the shopping cart. Another example of a software engineer and a data scientist working together is when a credit company uses a predictive model to score clients. Or an application for credit managers is developed to help them quickly score customers. 

Determining if you need a data analyst or data science team requires a practical look at the way your organization is operating. Pay attention to these high level indicators as well as consult a dc Analyst team member to learn more about how your company can benefit from gathering, organizing, and interpreting your data.

How to Analyze Data

After your team and data analyst have finished setting your objectives and gathering data you need to analyze your data to meet your objectives. When analyzing data you can use descriptive, visual, inferential, or modeling techniques. In this article we discuss various data analysis techniques and tools to use in analyzing your data.

Summarizing Data Using Descriptive Statistics

Descriptive statistics help you summarize and understand your data. There are different techniques for summarizing your data depending on if your data is categorical or continuous. Categorical data refers to observations that fall into distinct categories for example male or female. Continuous data refers to observations that do not have any distinct categories such as weight. 

When your data is categorical the most useful descriptive technique to use is count. You count the number of observations that occur in each category. For example, when you have one variable such as gender you count the number of people who are male and those who are female. When you would like to know the number of people in each category as a proportion of the total you use a percentage. In the gender example we can calculate the percentage of those who are male and the percentage of those who are female.  

As you summarize categorical data you are not limited to one variable. To summarize into categorical variables we use a cross tabulation. In a cross tabulation one variable forms the rows and the other variable forms categories. We then count the number of observations that fall in each category. If in our example we also have an education variable we would be interested in knowing the education levels of males and females. These education variables could be defined categories: no education, primary, secondary, college and university.  

For continuous variables there are descriptive measures that tell us how our observations cluster around a single value and those that tell us how our observations are spread. The mean and the median are two common measures that are used to summarize data. The mean is an appropriate measure when we have observations almost falling on either side. The median is an appropriate summary when we have most observations falling on one side such as our observations are skewed. 

If we collect observations on weight of adult patients we can use the mean to get the typical weight of a patient. If we collect observations on salaries we will have a few people earning much more than others, in that case the median would be a better summary. 

The minimum, the maximum, the range, and the standard deviation tell us how observations are spread. The minimum tells us the lowest observation, the maximum tells us the highest observation, and the range gives us the difference between the lowest and the highest observation in our data. The variance and the standard deviation tell us how a mean value varies. 

The confidence interval is calculated from the standard deviation and it gives us the upper and lower bounds of a mean value. When you have two continuous variables a correlation coefficient helps you understand the strength and direction of relationship. 

A negative coefficient shows you when one variable increases the other variable decreases. A positive coefficient shows you when one variable increases the other variable decreases. A correlation value close to zero shows you there is weak or no relationship. A value of 0.5 shows moderate strength while a value close to 1 shows you there is a strong relationship.

Visualizing Data With Graphs

There are different tools for visualizing categorical and continuous data. To visualize categorical data you use a pie chart or a bar chart. A pie chart divides a circular shape into angular portions that enable you to see the count or percentage of observations that are in each category. A pie chart can only be used to visualize one categorical variable. A bar chart helps you visualize categorical data using vertical or horizontal bars that show you the count or percentage of observations in each category. 

You can add the count or percentage of each category on the bars for easy comparison. Bars that are taller than the others show more observations in those categories. A bar chart can be used to summarize one or two categorical variables.

To visualize continuous observations you can use a histogram, a box plot, a scatter plot or a line plot. A histogram uses bars similar to a bar chart to visualize continuous observations. The key difference is that bars in a bar plot are for a single category while bars in a histogram show a range of values. A box plot summarizes data using a box and whiskers. The whiskers on both ends of the box plot show you the minimum and maximum observations in your data. Observations that lie beyond the whiskers are outliers.

The box shows you where half of your observations lie and within the box there is a line that shows you where the median lies. The histogram and box plot are useful for visualizing the distribution of your observations. The scatterplot helps you visualize the relationship between two continuous variables. It helps you visualize the direction and strength numerically shown by a correlation coefficient.

Making Inferences From Data

The techniques we have discussed so far help you summarize your data. To test hypotheses about your data you use inferential techniques. There are different techniques for continuous and categorical variables. 

A Chi-square test helps you test if there is any relationship between categorical variables. For example, in summarizing categorical data example we can use a Chi-square to test if education levels of men and women differ. For continuous variables we are mostly interested in the mean, where we can use T tests or analysis of variance (ANOVA). 

There are three variants of the T test that help us test if the mean of one variable differs from a target mean, if the means of two variables differ and if the mean of one variable differs at two different time points. ANOVA extends T tests by helping us test if more than two means are different. 

To help support the process of data analysis your data analysts will use both commercial and open source tools have been developed. Popular commercial data analysis tools include IBM SPSS, SAS, Stata, Excel, and Minitab. These tools provide a graphical user interface and a programming language for data analysis. R is a popular open source tool that is used to analyze data by writing programs. All of the tools and techniques we have mentioned support all the data analysis techniques we have discussed.

How to Organize and Wrangle Data

Your data analyst in Washington D.C. often begins each project with organizing data. Organizing your data makes it very easy to gather relevant information from your data. In an organization there is often multiple sources of data that need to be brought together to provide a complete view of your processes. 

The process of combining data from multiple sources into a single repository is referred to as data integration. For example an organization that sells products online needs to organize data on sales, store inventory, items returned by customers, orders placed from suppliers, and revenue for each product. Through data integration all of this data is combined and segmented to provide valuable insights. 

Understanding Data Integration

Data integration is one of the most important pillars of data organization. By performing data integration you are able to remove duplicates in your data, correct errors in your data, apply transformations on your data according to business rules, and store your data. When you have very small volumes of data you are often able to manually move them from different sources into a common store. However, large volumes of data manual integration is not viable so you need to rely on a dedicated data integration tool. 

Data organization requires a strategy and designed approach that is able to cope with the speed at which data arrives, its various structures, and volume. Data is often categorized as structured, semi-structured, or unstructured. Structured data is neatly organized into rows and columns. Semi-structured data has some data in rows and columns and others that cannot be organized into rows and columns. Unstructured data does not have any notion of rows and columns. 

Data volume often ranges from a few kilobytes to terabytes. Data also has the ability arrive at intervals of milliseconds, minutes, hours, days or even weeks. Depending on your data volume, structure, and frequency at which data arrives you need to select an appropriate tool.

Each organization has different data management challenges that cannot be solved with a single tool. Identifying the right tool or tools is a prerequisite for gaining value from your data. In this post we are going to look at three tools that you can use to organize and analyze your data: Excel, databases, and Hadoop. We will discuss situations in which each is appropriate. 

Excel

Excel is widely used for data organization and analysis because it is easily available and very user friendly. Excel is an excellent tool for organizing structured data with volumes that are able to fit within your system memory. With Excel you are able to easily perform calculations using formulas built in functions or custom built functions. With PowerQuery you are able to extract  data from different sources into Excel without much coding required. Excel also enables you to  import data from the web from databases such as a SQL Server and Mysql. Importing and integrating data from files such as .csv, .xml, text files, Azure, and Excel data tables and other data sources is also simplified. 

Once your data is in Excel you may begin analyzing it with other compatible tools in the Excel software family, known as add-ons. For example, with PowerPivot you are able to analyze your data using pivot tables and pivot charts. With PowerView you are able to visualize your data using charts and maps.

Databases

Excel helps you organize and analyze structured data that fits within your system memory. When your data cannot fit within your system memory or it is not structured databases are the right tools. Databases are able to handle gigabytes or terabytes of data. They are often broadly categorized as relational (SQL) databases and NoSQL databases. Examples of relational databases are Oracle, SQL Server, Mysql, IBM DB2, and PostgreSQL. Examples of NoSQL databases are MongoDB, Cassandra, and HBase. 

Relational databases are suitable for organizing large volumes of data that are structured. They have a language referred to as SQL which is used to manipulate the gathered data. With relational databases you are able to import data from other relational databases and business applications like CRM and user friendly files such as .csv, text as well as other mainframe databases and legacy applications. 

To integrate data from different sources into your relational database you can use SQL or rely on a dedicated data integration tool. Data integration tools help your data analysts source your data, clean your data, and load your data into your database. Once your data has been cleaned and stored in your relational databases you can use business intelligence applications for data analysis and visualization. Examples of business intelligence applications are IBM Cognos, Tableau, and Qlikview. 

Additionally, NoSQL databases are suitable for organizing very large volumes of data that are stored on multiple servers and are semi-structured or unstructured. NoSQL databases are an excellent choice when relational databases cannot handle the frequency, volume, and variety of data that needs to be organized and analyzed. 

Hadoop

Hadoop is an ecosystem of data management tools that have been developed by Apache to handle growth in data because existing tools could not handle the volume, variety and velocity of data. Often data of such magnitude is difficult to host on a single server. Therefore Hadoop was developed as a system that is able to process data stored on hundreds or even thousands of servers and it has been a success. 

The software is designed to handle very large amounts of data; whether the data is structured or unstructured. With Hadoop you are able to process data whether it continuously streams in or arrives in batches. It has a specialized file system referred to as Hadoop file system (HDFS) for storing data. 

Hadoop has the tools to import data into HDFS and export data out of HDFS. The data movement tools enable your data analyst to get data from different sources such as databases, social media, web, and files. Add-ons such as Sqoop and Pig enable you to source data and move it into Hadoop. Sqoop specializes in exporting data from relational databases to Hadoop. Pig enables you to source structured and unstructured data and import it into Hadoop. Once your data is in Hadoop you are able to write MapReduce programs in Java to analyze your data. 

As data analysis continues to evolve more and more tools are introduced to support data gathering, structuring, and presenting. For example, due to the complexity of writing MapReduce code various tools have been developed to simplify data analysis in the platform. Hadoop Hive enables you to develop data warehouses and analyze data using a similar language to SQL. Or to discover patterns in your data you can use Spark to develop machine learning algorithms. To manage structured and semi-structured data within Hadoop you use Hbase. These are some of the tools you can use to organize and analyze your data when captured by Hadoop, that simplify the process.

Selecting the right tool to organize and analyze your data is very important in understanding your data. With the right tool you are able to reap the benefits of insights within your data and thus help you reduce waste, improve customer retention, and develop reliable business strategies. Armed with knowledge from your data you are able to make more informed brand decisions.

How to Gather Data

When you need to make informed decisions you need to rely on accurate data provided by a data analyst. To gather accurate data you must begin by collecting, analyzing, and interpreting the right data. In an order to collect the accurate data you need to follow an organized and systematic way of gathering all the pieces of information from the sources available to you. When gathering data you can collect quantitative data, qualitative data, or both. 

Quantitative data are observations that are expressed in numbers and you can meaningfully summarize using statistical techniques. For example, the number of visitors to a website is qualitative data.  Qualitative data provides you with descriptions and you cannot summarize it meaningfully with statistics. For example, if you ask your customers why they decided to purchase your product rather than your competitors, you will get qualitative data. In this article we discuss how to gather data from Google Analytics, Adobe Analytics, social media, and point of sale transactions.

Preparing to Gather Data

Before you begin collecting data there are several preliminary preparations and best practices provided by leading data analysts. 

1. Identify the issue to be addressed by collecting data and set your objectives. 

2. Make a decision on the approach and methods you will use to gather data. Decide on who or what you will collect data about, your groups of interest, and geographical areas from where you will the collect data. 

3. Make a decision on type of data you will collect. You can collect quantitative, qualitative, or both. 

4. Identify your sources of data. You can decide to collect your data from one or multiple sources. Make sure your data sources help you meet the objectives you set.

5. Estimate the period you will take to collect your data. 

Types of Data

There are various forms of data available to collect, organize, track, and analyze data. Today the most essential data is captured online and observes visitor behaviors, locations, and other general information. We’ve compiled a list of the most popular data gathering platforms and services, as well as provide insights into the best ways to benefit from the information they provide.  

Google Analytics Data

Google Analytics (GA) is a service provided by Google to assist marketing and website development teams with understanding the traffic patterns and behaviors of visitors. GA helps you collect data on total visits, traffic sources, bounce rate, and ecommerce goals. This data helps you measure return on investment and build conversion strategies that are feasible and slightly proven, which may reduce risk.

Before you can begin collecting data via GA you need to add a tracking code on your website pages. The tracking code is a piece of JavaScript code that you place within the head tag of your page. Another way you can add a tracking code to your pages is by using Google Tag Manager (GTM). With GTM you avoid the problem of modifying code.

Using a tracking code is the classical way of gathering data but Google is encouraging users to migrate to Universal Analytics (UA). UA is just an updated version of GA that gives you more features. 

By default GA gathers data on the web page, browser, user location, and language but this is not the only data you can collect. Using JavaScript code or GTM you are able to customize the data that will be collected. By customizing the data you collect, you only gather data relevant to your website so you are able to create reports that are useful to your business.

The best way to harness the power of GA or UA is to partner with your data analyst to determine the most useful data to your business. 

Adobe Analytics Data

Adobe Analytics is a web analytics solution that works in a similar way to GA. To collect data on Adobe Analytics you need to add JavaScript code in a similar way to GA. Adobe Analytics provides Dynamic Tag Management to help you manage your tags without any need for coding skills. 

Although GA and Adobe analytics are similar they have their differences. GA offers a free and a paid version while Adobe Analytics only offers a paid version of its analytics platform. GA stores your data for up to 25 months while Adobe Analytics stores your data for as long as you are a customer. 

Adobe Analytics enables you to collect data from websites, email, mobile devices, client-server devices and most devices connected to the internet. The tracking code that facilitates data collection can be placed on the client device or on the server side. Many data and business analyst find this information very useful and concise. 

When deciding whether to place your tracking codes on server or client side consider the following issues:

1. When JavaScript is disabled on a browser it may not be possible to collect data.

2. Device limitations of inability to run JavaScript may prevent gathering data.

3. Sensitive data needs to be protected so that it is not viewable on the browser.

Social Media Data

Social media data is information we gather from social networks that tells us how users are viewing, sharing, and engaging with our content and profiles. Some of the social media data that we can gather are number of shares, likes, mentions, followers, and comments. From our social media data we are able to calculate key performance indicators (KPI) that tell us how the brand is performing. Facebook pages give you KPIs on engagement, likes, impressions, and other valuable metrics. 

To successfully use social media data the first step is to identify the goal that will be achieved by using social media data. For example, you can target to improve customer service by analyzing user sentiment. Another thing you need to be aware about social media data is its limitations. For example, some social networks are skewed in terms of gender or age of their users. Any data collected from such networks will therefore not be representative of an actual situation. Once you have identified KPIs that are important to your business goals you can rely on tools available in the market to help you gather data. 

Point of Sale Data (POS)

Data collected at a POS can be identifying or non-identifying. Identifying data includes details like names, email, physical address, and phone number. Identifying information is very useful because you can use it to link data existing in other systems. Non identifying information like if a customer is a parent; is also useful to the business and may be easier to acquire. 

Personal data is stored in a POS system together with products or services purchased. The collected data is then moved to a purpose built system like a data warehouse or a CRM from where it can be sourced to make informed decisions. 

When you are moving data from your POS system to your purpose built system there are three important aspects to consider:

• Make sure the data you are moving is of good quality. Completeness, accuracy, and validity are the three quality aspects you need to look out for.

• Update data as often as possible. Daily updates are good but an even shorter update interval is better, such as every 12 hours. 

• Make sure the purpose built system where you move data has the capacity for analysis and extraction of actionable information from data. 

By collecting, analyzing and acting on data from multiple sources you are able to have a complete understanding of your customers. With a good understanding of customers you are able to personalize your offerings. As you sit down to determine the type of data you need remember to consult with your data or business analyst to ensure the data you are gathering is relevant and useful in achieving your overall objectives.

How Big Data Can Be Used for Your Business

The expression "data analyst" summons pictures of a solitary expert working alone, applying obscure recipes to boundless measures of information looking for valuable insights. Information investigation is not an objective in itself. The objective is to use the information to empower your business and develop and craft strategies that improve operational efficiency and profit margins.

In this article we take a look at how an experienced data analyst in Washington, D.C. is qualified to give your business a competitive advantage against your competitors through the analysis of big data. 

Embracing Big Data for Your Business

Big data is an all-inclusive term that often refers to large amounts of information. Traditional data is typically relational data found on internal databases. Conversely, big data is collected and generated in large volumes and varies in frequency, variety, volume, and value.  

In most small to medium sized businesses big data is a moderately undiscovered resource that organizations never exploit yet should. Big data does not simply provide trendy information and statistics, but rather genuine case studies that uncover what's going on now, and what is prone to happen in the future in each niche group. These glimpses into the future provides your business with the opportunity to gain insightful projections that prove valuable during quarterly planning events and development. 

Big data use and implementation is best observed in the accomplishment of online powerhouses, such as, Amazon, Google, Facebook, and eBay. Through proper big data sorting, development, and monitoring each online influencer provides a detailed account of just how effective and useful big data is. For example, through the analysis of big data Facebook is able to position local businesses in front of consumers most likely to buy based on online behaviors, locations, and interests. Likewise, Amazon provides detailed shopping recommendations based on transactional coincidences, search, and behaviors within the website. 

Understanding Big Data

Without the proper application of big data many companies fail to harness the power of innovative databases that are readily available to them. To understand how big data is utilized in general you can equate big data analysis as the same process employed for drilling oil. When an oil rig works to drill for oil it begins by slowly chipping away at the surface. Once the surface is successfully penetrated with the least amount of surface or environmental disruption the rig begins to drill deeper and harder. Often the deeper the drill digs the more accelerated the pace. 

As so it is with big data. The more information there is to sort and analyze the slower the process is likely to be. Initially, identifying the type, variety, and volume of the data sets the tone on the types of outcomes to expect from the data and how it is most likely to benefit your business.

As you undertake the task of gathering, sorting, and analyzing big data remember to allow your data analyst in Washington D.C. to work at a pace that makes sense for your business, objectives, and information. Consider that our digital era produces an alarming amount of data and information every second. The rate at which this information is gathered and sorted duplicates at regular intervals, consistently daily. 

Types of Use for Big Data Findings

Once your business has embraced the value of big data you must determine what to expect and identify tangible outcomes that can be derived from this data. Begin by asking general questions such as what sort of significant worth big data in transactions and shipping are likely to provide. Or asking particular questions to better identify your target market.

From a quality perspective, the utilization of big data is often categorized as one of three measurements: 

Effectiveness

Operational effectiveness directly affects the way your business runs, interacts with customers, and supplies your products or services. For this situation, information is used to settle on better choices, to upgrade asset utilization, and to enhance process quality and execution. Ensuring that your business is effectively operating and serving customers efficiently is the first step in keeping your company competitive and savvy within your industry. 

Often discovering how your business is operating and identifying various areas of improvement saves time, reduces downtime, and conserves your resources. As your data analyst works through big data trends and outcomes, various patterns emerge that offer key points on how to simplify your operations for optimal interactions with each transaction or project.

Experience

The second measurement is client or customer experience. The largest point and purpose is to increase or improve customer loyalty, perform exact client segmentation, and improve client or customer handling. Big data pushes CRM strategies and helps all customer oriented practices to be relevant, controlled, and measured.

Likewise it also empowers you to discover new plans of action to supplement income streams from existing items, (also known as upselling), and often propels thoughts of innovation to improve customer experiences and sales funnels. When using big data to improve the way your customers or clients interact with your business you gain valuable insights that help you to strengthen your brand and authority within your industry organically as well. 

Marketing

Data driven advertising continues to grow in popularity as more and more business turn to digital marketing. When big data is used to identify target audiences the return on investment is often positive. Marketing data sheds light on how your business can create relevant messaging, identifies proven pain points that your company solves, and gives you a competitive edge against your competitors. Big data is compiled in many ways such as via mobile behaviors or website traffic patterns.

Utilizing data gathered based on how visitors interact with your website, how many conversions your sales page secures, or how long a user spends on a webpage on your website provide valuable tips on how to build your next campaign.

Big data is one of the most essential parts of any business. As your business begins to embrace the use of big data, you are sure to learn more about your business than ever before. Some of the most simple and reliable ways to acquire big data is to first examine your internal databases and then explore industry norms and standards. Your data analyst will sort, gather, structure, and present this information to help you gain a competitive advantage in your industry, as well as provide you with a solid starting point for any projects or operational changes on your calendar.

Not sure where to start or if you need a data analyst? Read our quick post, How to Use Data Analyst for Your Business to get started.

4 Types of Data to Transform Your Marketing

Developing a business strategy is no major ordeal. Yet creating one that effectively infers relevant data to enhance the businesses operations and sales is not something every data analyst in Washington D.C. is capable of. Knowing how to accumulate information is one thing, while realizing what to do with that information to change your business is a by and large a more diverse story. 

To utilize and structure data successfully it is vital to employ the right data analysts within your business and organization. Relevant information on a very basic level changes the way your organization contends and operates within your industry. Organizations that put resources into effectively analyzing data often gain esteem, confidence, and traction from this information. Leading analysts refer to digital data collecting ecosystems as the new structure of business. As industries continue to embrace and foster digital interactions with customers, the role of data continues to be challenge to keep up with, yet essential when protecting your competitive edge.

McKinsey & Company has noted the increasingly critical role that business analysts are playing in business today. A recent survey of 714 companies around the world revealed that ROI for investments into analytics pays off. The company explains, “Our findings paint a more nuanced picture of data analytics. When we evaluated its profitability and value-added productivity benefits, we found that they appear to be substantial—similar, in fact, to those experienced during earlier periods of intense IT investment. Our results indicated that to produce these significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities.”

Connecting Data and Marketing

Marketing data lies at the beginning of each fruitful marketing methodology. Data guides businesses and reveals various important starting points. The best data analysts in Washington, D.C. are capable of recommending who your best clients and prospects are, how to target them, how to build the right offers, and identify the right channels on which to present those offers. Additionally, your data analysts is experienced with recommending which messages drive the most changes, and how to improve customer retention.

As you work to achieve a specific marketing goal and develop your next marketing campaign, you initially need to completely comprehend who your clients and prospects are. This information and knowledge must go beyond basic demographics such as: names, addresses, telephone numbers, annual salaries, and email addresses. Customers and prospective clients anticipate that you know who they are, what they need, where to find them, and the best time to speak with them. To begin you must first gather relevant information and digest that information to aide in the launch on your next marketing campaign.   

Understanding how to use data in your next marketing campaign requires that you identify the most essential data needed prior to your launch. In general, this data is compiled by your data or business analyst in Washington, D.C. Through proper data analysis, planning, and implementation your next marketing campaign is much less likely to fail. Here are the 4 types of data you need when developing your marketing position.

Identifying Your Target Market

Begin by assembling factual data in regards to your target market such demographics, market fragment, their needs, and shopping preferences. Utilize your exploration to elucidate how to reach your prospective customers. Ask various questions to build a better profile of your customers such as age groups, gender, employment status, disposable income, and familial relations.

Also gather relevant information from your current business operations. Identifying the times of day your business or website is most profitable often sheds light on when your marketing campaigns should be launched. Taking into consideration the average transaction amounts and use of coupons or special offers provide insights into how your company is positioned within your industry.

Build a SWOT Analysis

The SWOT analysis is an acronym for strengths, weaknesses, opportunities, and threats for an organization or business. It was developed by Albert Humphrey during his tenure at Stanford University in 1960. His original goal was to identify why corporate planning failed. By embracing the SWOT analysis for your business you identify your competitor’s strengths in your industry, areas of improvement, and possible areas in which they fail. Knowing how to find this information takes some know-how. Your data analysts compiles this information to give you a solid approach on how to launch as well as what to expect. 

Gathering information on how your competition runs their marketing campaigns and positions their products is vital to developing successful marketing campaigns for our own brand. Consider various questions such as: what is their value proposition, how does your company differ and offer more, what do you like and what don't you like about their showcasing effort?

Price Your Products or Services Competitively

Very few organizations are able to set a price for their products or services without considering various costs such as shipping, manufacturing, and supplies. For service oriented companies these cost include operational influences including invoicing, ongoing training, and time. These cost directly affect the pricing of your competitors as well as your own. Gathering relevant information and insights into the cost of operations and current market trends provide you with the right data to build a successful and profitable pricing strategy. 

Research Your Marketplace

Whether you offer a service or product, it is critical to comprehend what is available to your customers at the time you plan to go to market. By gaining solid insights into the current offerings and practices of your competition you are able to identify the best ways to present your products as well as to whom. 

Researching your marketplace is often a combination of utilizing the information prepared in your SWOT analysis as well as your target market. As you learn more about your marketplace you are able to position and enhance your product or service in view of discoveries about what your prospective customers truly need and want. Concentrate on things such as capacity, appearance, online presences, and guarantees. 

Where is the best place to launch your product or introduce your service? Where would it be a good idea for you to disseminate from? Is a retail establishment the best stage for your item, or are your needs best met online?

Detailed and properly prepared market research data is one of the most important parts of any marketing strategy. The work of your data analyst in Washington, D.C. gives you a simple road map on how to position your company in the marketplace and thus avoid complete failure. Market research guarantees that your business understands your industry sector patterns, demographic moves, and adjustments needed as the economy shifts.

Data is a valuable asset in any sort of marketing and should never be overlooked. With consumers becoming more aware of the vast amount of offerings in the marketplace your statistics and data must be structured and compiled in a way that allows you to build a marketing campaign that engages, targets, and converts prospects. If not appropriately maintained and analyzed regularly by an experienced data analyst, you risk the chance of  diminished productivity, product launch failures, and lack of direction or purpose. 

Consult with an experienced data analyst from dc Analyst to learn more about how to utilize the information your company currently holds, as well as how to compile other data necessary to identify your target market and develop your pricing strategy.

9 Essential Skills Your Data Analyst Must Have

Data analysts are information translators that possess skill sets which greatly influence your business operations, sales goals, and various other projects. Through data analysis and understanding your business’s trends, patterns, and insights give you have the ability to simplify your sales funnel, reduce waste, and improve productivity. There are a few essential skills sets to look for when looking to hire a data analyst based in Washington, D.C.

In this article we touch on the most essential skills every data analysts needs in order to provide your business with insights and information that is relevant and conclusive. For information and detailed tips on how to use data analysts for your business read our short guide here. 

Organized and Detailed Work Ethic

When analyzing data even a small mistake can make a huge difference further along the line. If compiling, gathering, and segmenting information is performed in a haphazard way by your data analyst you could end up with misleading results and inconclusive solutions. Inaccurate information can point you in a false direction, waste time, and impact your ability to reach your goals.  

Given the importance of receiving and interpreting reliable data it is essential that your data analyst organize, structure, and present data in various forms. Prior to hiring your data analysts in Washington D.C. request to view spreadsheets, reports, and presentations. Check these items for details, structure, and relevant visuals.

Interpret A Database Query Language

It is often necessary to extract data in order to perform analysis operations. To do this well your data analyst should be experienced in a database query language such as SQL. This skill set ensures that the information is easily pulled upon request as well as analyzed properly. There are various forms of database query languages used today. 

Speak with you prospective data analysts about the languages they are well versed in. Some of the most popular softwares today include Apache Hive and Pig, and SparkSQL.  

Other statistical languages and packages that can be used by your analysts include SAS and SPSS. Being well versed in tools and languages ensures that your analyst is able to work effectively with minimal training and oversight. Additionally, having a command in various software languages and programming is a relevant skill for a data analyst who will be required to work with vast amounts of data frequently. Often these analyst hold certifications in select softwares and programming languages.

Proficient in Arithmetic

The saying goes, “Arithmetic is to mathematics as spelling is to writing.” Arithmetic is something we all learn from an early age, however, many of us stop at algebra or geometry. Your data analysts should be proficient in handling numbers and equations. Arithmetic is one’s ability to manipulate number sequences and often deals with calculations. Truly experienced data analyst are confident in calculations, sequencing, and numerical statements.  

As you seek a business and data analyst for your next project discuss the various forms of arithmetic they have engaged in from past 3 years. Choose the candidate with a strong background in arithmetic and experience in working with numbers on a regular basis. This skill set also simplifies how your reports are most likely to be presented and structured for easy understanding.  

Create User Friendly Spreadsheets

The ability to build and structure a spreadsheet is a basic requirement that is often overlooked.  It is vitally important for your data analyst to be able to take the information discovered and present it in a way that your entire team can understand and interpret. Spreadsheets with various headings, pivots, and charts are difficult to navigate for many users. The use of formulas, tables, and sorting should always be utilized to ensure your team is on one page. 

Your data analysts must be able to carrying out advanced tasks on a spreadsheet in order to manipulate the figures in numerous ways. It is not enough for a data analyst to just be able to put data into rows. Your project is likely to require a analysts that can complete tasks such as making reports and creating dashboards. 

Firm Grasp of Statistics

A data analyst must have a well-rounded knowledge of statistics. This allows them to determine when to use different techniques to achieve the required results. Identify a candidate with a sound knowledge in areas such as statistical tests, maximum likelihood estimators, and hypothesis testing. The ability to implement and utilize statistics for any project gives you sound results that are supported based on various factors such as annual data or industry trends. 

Ask your prospective data analyst how they plan to go about gathering statistics and using the information when preparing reports and summaries. Also consider asking them to research basic information and data about your industry and niche. Present this information to your team for review prior to beginning your project. 

Perform Data Wrangling or Munging

Data wrangling and mungling involves taking raw data and translating it into a form that is more conveniently consumed by semi-automated tools and programs. It is essential that your analyst possess this particular skill if you business has yet to implement a completely digital operating and sales process. Wrangling or munging involves finding a way of converting or mapping data that is presented in inconsistent formats. This makes raw data easier to work with and can save a lot of problems later on in the process.

Present your most challenging data scenario to your prospective analysts. Pay close attention to their ability to interpret, structure, and deliver the information in a more convenient form. 

Data Visualization Techniques

This essential skill simply answers the question: how will your data analyst present their findings to you? You need your business analyst to possess the skill set to present solutions in an easy to navigate and understand presentation. Visualization tools such as d3.js and ggplot are examples of the kind of visuals that you will want them to be familiar and well versed in. 

Flexible Communication Skills

At some point you will need to make big decisions based on the work carried out by your data analyst in Washington D.C. This means that you need an analyst who is excellent at communicating their findings with colleagues from different backgrounds, departments, and focuses. A good data analyst should be comfortable presenting presentations to both technical and non-technical members of your team. 

It is easier to evaluate communication skills in person. During your interview pay close attention to your data analyst’s ability to make eye contact, confidence, and speech patterns. Do they speak fast? Are they using language that is easy to understand?

Efficient Time Management

Hand holding for business owners is a nightmare. It is even more so when engaging the help of a data analyst. Selecting an analyst that has a proven track record of completing projects prior to due dates or within required time frames ensures that your data will always be present, up to date, and relevant as you require it. 

Efficiently managing time involves not simply just compiling and analyzing data but also presenting the data for your team to use when needed effectively. Ask your prospective analyst to provide examples of when they completed a project quickly with positive outcomes or samples of the presentation.  

Aside from a firm grasp on how to navigate business intelligence systems and analyzing information, your data analyst should also be proficient in these various other skills. An inexperienced data analyst may cause you to make mistakes in your everyday operations, delay launching a product, or reduce productivity. 

To find a data analyst that meets your needs and possesses these essential skills consult with dc Analyst to get started.

How to Use Data Analysts for Your Business

Information investigators, commonly known as data analysts, play key roles in a range of tasks that involve gathering, arranging, and interpreting measurable data. The nature of a data analyst in Washington, D.C. varies from business to business and project to project. For example, a hospital data analyst concentrates on performing financial analysis of the hospital’s operations, physician, and ancillary rates. While an audit analyst works to ensure compliance and maintain litigation files that track progress and results. 

Value of Data and Information

Data analysis or information examination is vital to businesses because it involves developing methods of assigning numerical values to various business operations. Many analysts are uniquely qualified to recognize efficiencies, identify waste, and recommend conceivable upgrades to policies and operations. Indeed, no business survives without breaking down accessible information, and compiling data that gives them legs to remain competitive in their industry and market. 

The application and analysis of data is broad. An organization may need to introduce new variations of its current line of natural juice. A data analyst in Washington D.C. could be charged with compiling relevant factors, and provide avenues on the best way to launch the new products as well as identify key markets. Likewise, a sales executive of a manufacturing company realizes that there is a major inefficiency in a division’s supply chain. A data analyst is capable of identify where the inefficiency begins and recommend the most profitable and cost effective ways to improve efficiency and reduce waste.   

Whether you need to make key decisions on your next marketing campaign, launch a new project, or improve your everyday business operations, data analysis is an effective way to solve many key issues and challenges your business is facing. Through data you may be able to answer some of your most perplexing operational questions such as the percentage of clients that are most likely to give you repeat business. Or develop a target persona for your next big product launch. 

Role of Data Analysts Today

Simply analyzing information is not adequate from the perspective of settling any decision or forward business strategy. How you translate and implement analyzed information is also vital. In most cases, data analysis provides a basic decision making framework, however, an experienced data analyst in Washington, D.C., also develops a supporting and simplified implementation and overview summary. This report often organizes discoveries, breaks a large scale profile into more easily digested material, and identifies important insights from the dataset to equip your team with the most essential information needed. 

Data analysts simplify and interpret numbers into plain English. Every business gathers information, from statistical surveying to transaction figures to transportation logistics to suppliers. Data analysts gathers this information and uses it to help organizations improve operations, reduce waste, and better serve clients and customers. 

It is also important to differentiate the role of data analyst and business analyst. The terms data analyst and business analyst are frequently utilized interchangeably. At larger organizations a data analysts plays a key role in setting the future strategies of the company. If employed by a smaller scaled business a data analyst or business analyst in Washington, D.C. often provide similar services. However, business analysts employed by larger organizations focus more on the everyday operations and procedures of a business and how to improve efficiency and cost. 

How Data Analysis Works  

Most data and information is stored in a digital database or framework and accessed via a device or computer. Data analysts gather information in a variety of ways including visiting the site of the project, independent research, and an in depth look at client files, transactions, and customer accounts. Information is often compiled on location or from a remote office. Most analyst work traditional office hours, however, depending on the project and time frames an analyst may be required or requested to work a weekend or on call. 

Data Collection. A standout amongst the most essential things any data analyst does is gathering, sorting, and studying distinctive arrangements of information. Their core focus is nailing down a settled overview of the information. This overview is often surveyed and observed over time and during planning and development stages. 

A supermarket may request that a data analyst gather the hours that specific representatives work alongside net revenues for certain days, weeks, or even hours to determine the team’s profitability. An ecommerce store might need to identify hard numbers on where visitors are originating from, the amount they are spending on buys, and whether bargains like free shipping have any bearing on overall earnings for the business.

These are a few distinct ways a data analysts is employed by businesses looking for insights and answers into questions that affect sales and expansion. The information is often controlled, standardized, and adjusted for implementation. Data analysts ordinarily utilize PC frameworks and complex count applications to get their numbers nailed down, yet there is still a ton of scholarly know-how that goes into making these frameworks work. 

Extrapolation and Interpretation. Experienced data analysts often develop summaries of what the data implies and shares relevant insights with responsible parties within the business or organization. Obtaining hard numbers on deals figures for a given Christmas season, for instance, is to some degree helpful all by itself; however, it is typically most profitable when these figures are stacked against numbers from earlier years or different seasons as a state of correlation. 

Business analyst in Washington, D.C. are often approached to assists entrepreneurs and businesses with contrasts in numbers from year to year or from location to location. Data analysts more often than not have the aptitude to identify measurable qualities of things, as well as clarify what they mean. 

Projections and Advisory. Data and business analysts are usually charged with advising leaders and management on how certain information can be used to change or enhance operations. These improvements and recommendations are necessary when considering rolling out improvements and other changes. An example is a hospital that is looking to improve patient release time. A data analyst may observe operational patterns, insurance billings, and types of procedures to determine possible cause of delays and how to address them.  

Is A Data Analyst Right for Your Business? 

The majority of businesses and organizations can benefit greatly from the insights and expertise of a data analyst in Washington, D.C. Information investigation plays a vital role is identifying areas of improvement in operations, marketing, and efficiency. Analysts in the fields of advertising, sales, and logistics utilize data to discover market white space, optimize supply chains, and improve customer retention. 

Through the proper application of information your business can understand customer patterns, eliminate downtime, and maximize time. Whether launching a product, determining the best methods for shipping, or identifying the best location for your next store; a data analyst can provide the information you need to make the most informed decisions based on various factors. These factors can include demographics, community statistics, and relevant competitor analysis. 

Determining the role of data analyst in your company is sometimes a challenge.  It is always recommended that you meet with a data or business analyst to gain practical insights into how your business may utilize data and information to remain competitive in your marketspace.