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 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.