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!

How to Present Data and Findings

Modern business operations generate a variety of data from processes such as sales, customer relationships, human resource management, and product ordering. These multiple data sources are brought into a single repository. Often data analyst create reports for decision makers to aide in decision making and organizational planning. 

Business intelligence (BI) tools are used to identify insights from data repositories. These BI tools connect to different data sources and enable data analysts to equip decision makers with relevant insights from the data. BI tools offer features that are useful for reporting, querying data, online analytical processing (OLAP), and data mining. In this article we will discuss each  BI activity and how they are supported in Tableau, QlikView, and Excel. Lastly, we will look at how PowerPoint can be used to prepare presentations to effectively communicate findings. 

Reporting and Querying

Business reports are pre-defined ways of understanding your data. These reports are delivered on a regular schedule, such as weekly, or upon request. Reports are predefined. Using data querying you are able to select the type of data you would like to see. Reports and queries are easily visualized using cross tabulations and charts. In a cross tabulation the information is presented in rows and columns. Other ways to present data include charts such as pie, bar, and histogram. These tools help you understand your data and key performance indicators. 

One of the most important parts of data are key performance indicators. To present a set of key performance indicators (KPIs) that provide a high level overview of your business dashboards are used. Just like in a car dashboard you are able to view all aspects of your business on a single location. A dashboard can contain business metrics displayed in charts and graphs, maps, KPIs, RSS feeds, and any other content that is viewable on the web. These dashboards can be updated daily, in real time, or via a monthly sales summary report. 

OLAP

OLAP is a technique for exploring data interactively such as when you observe something interesting in your data you can immediately continue exploring the data to get answers. Using OLAP you are able to see data from multidimensional perspectives and drill up or down to view less or more details. Using OLAP a sales analyst can view sales data from one state for the month of April and the compare sales of the same product in August in comparison to other products that were sold.   

Data Mining

Data mining is a collection of techniques that is used to understand data stored in databases. With data mining you are able to identify data anomalies, patterns, and relationships that exist in your data. Armed with this information you are able to grow revenue, reduce costs, identify fraud, improve customer relationship, and reduce risk exposure. With data mining we are also able to accomplish useful tasks such as predicting customers who are likely to purchase a product, transactions that are likely to be fraudulent, and possible cyber security breaches. By taking action on such insights your data analyst will provide recommendations on how to improve your business outcomes. 

Tableau

Tableau is a BI tool available for use on a desktop, mobile device, a server, or as a hosted solution. With its availability on these various platforms it is an excellent tool for understanding and navigating data. With Tableau you are able to source data from files, relational databases, and Hadoop. Tableau has an excellent support for data reporting and visualization. 

With Tableau you are not limited to reporting on raw data as you can perform calculations and use calculated fields in your reports. Simple and advanced data visualization features like waterfall diagrams, box plots, bump plots and histograms among others are supported. 

Dashboards are very well supported in Tableau. For complex statistical functions not supported within Tableau you can easily use R. Integration of R and Tableau means you are easily able to implement data mining that enables you to understand hidden patterns in your data.

QlikView

With QlikView you are able to import data from different sources including files, the web, databases, and custom data sources. QlikView can be broadly divided into two parts which are the front end and the backend. The front end is a web browser based interface that enables users to explore and interact with data. The frontend has a QlikView server for viewing already created business reports which makes it easy to provide versatile reports. The back end is made up of QlikView desktop and QlikView publisher. 

The desktop is used to create report templates which are viewed using a web browser. The publisher is used to distribute reports by controlling users who are allowed to view content and the type of content they can view. With QlikView you can analyze data using cross tabulations, charts, and statistical tests. Reporting, querying, and dashboards are very well supported. 

Excel

Business Intelligence capabilities in Excel are almost at par with those of specialized tools because of features provided by Power BI. These features or add ons include Power Pivot, Power View, Power Map and Power Query. With Power Pivot you are able to import data from other spreadsheets, files, and databases. After importing data you can do analysis. Power View is the dashboard creation solution in Excel. 

After creating a Power Pivot connection to data you are able to analyze your data using interactive reports and views. The charts, maps and tables created with Power View are interactive therefore you can drill down and segment to better understand your data. Once you have created dashboards you can present them within Power View or use a specialized presentation tool like PowerPoint. To visualize geographic information you use can use Power Map. 

With Power Map supports OLAP in Excel and is very advanced. You are able to connect to Microsoft and non-Microsoft OLAP data sources as long as they offer OLEDB for OLAP support.  Keep in mind that analysis of OLAP data is only possible using a Pivot Table or Pivot Chart. 

PowerPoint

PowerPoint provides all features necessary to create presentations that effectively communicate insights from your data. It is most commonly used by data analyst. PowerPoint being a Microsoft product integrates very well with BI features in Excel. Dashboards created with PowerPivot are easily exported to PowerPoint. QlikView offers a plugin to help with the creation of PowerPoint presentations of charts and dashboards. Tableau offers features to export your visualizations as pdf files and also create PowerPoint presentations. 

Presenting your data is essential for understanding your data. Data analysts must present recommendations and insights gathered from data to do a variety of things such as improve operations or project next quarter’s sales. 

At dc Analyst we understand what it takes to present your findings and data in a way that makes sense. Our analysts can help you learn the basics of presenting data and findings to help you communicate your findings with your entire team.

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.

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.

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.