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.
