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The Data Model You've Been Searching For: Lessons from Data Analyst

Updated: 20 hours ago


Data Models can make or break your company. At Reiz, we are always on the precipice of progress by making sure we have all our data points and insights covered before deciding about the company's future.


In this article, you will understand how Data Analyst Vitaliy Derevlov helped produce a Data Model here at Reiz, as well as the importance of having it in place in other businesses.


Creating the Reiz Tech HR & Financial Data Model


When asked about his personal experience crafting the HR & Financial Data Model at Reiz Tech, Vitaliy Derevlov told us that “Reiz already built an almost optimized financial model that can handle high-level tasks and objectives.”


Though it was not enough now to answer the burning questions of the management Vitaliy and his team added more features to the financial model, and it can now “Ensure detailed analysis of projects, assessment of the need for Services and Roles, including team dynamics & forecast.”


“Our data model can help us control dynamic changes in cost, price and margin of Services and Roles. By having this function Reiz can give projected portfolio analytics and assess the balance between the clients' need for team Seniority Level helping us understand the price and cost analysis of clients and projects, searching for the possible gaps.”


As for the human resource model, “it can now monitor the Dynamics of the number of employees, by projects/services/positions/Seniority Level. By analyzing the stability or volatility of the Reiz Team, this model can give insightful reports on each team’s productivity levels. This can help the company improve employee Life Cycle monitoring, and analyze HR diversity indicators which give Reiz detailed analytics on why employees leave.”


“With the goal of understanding - whom we're losing (in terms of Seniority, Lifecycle, Price/Cost/Gross Margin per hour) our model also helps report employee distribution by Price/Cost Groups, Employee utilization efficiency in terms of the price of positions and the cost of employees. Our model also can monitor personal efficiency of employees (change by Seniority Level, Price rate per hour, Cost rate per hour).”


By adding these features to the model Data Analyst Vitaliy Derevlov and his team have assisted in giving us greater insights into what makes our business profitable as well as beneficial.


Making A Purposeful Data Model


When asked about what makes a data model good and purposeful, Vitaliy retorted that from his point of view, “A fully completed Data Model creates the opportunity for businesses to build the systematic management and development of all company processes, which involved in overall chain of business value creation. From working with suppliers and employees at the hiring stage, to the management of financial flow, planning, forecasting, and marketing communications with target audiences.”


As for why he believes that having data models is essential for every company, he says that “complete Business Data Models can and should answer the following key questions involving the current state of the business.”


“Business models should be able to identify key trends and patterns of business development. By doing so, it can help the company grow by understanding events that have a major impact on the business. This can be implemented through forecasting trending business directions and having insights on the current business potential.”


Through the data models, impactful decisions can be made, which will allow a positive projection of the company's future. Using its internal potential to attain the set sustainable development goals.


If your business data models can answer these questions, “you will always know what's going on and how to respond in any situation that your organization finds itself in”.


Creating A Data Model


On whether it was easy to make a data model Vitaliy replied coyishly with a “Yes and no. From one side, previous experience can form approaches to business model creation and identify key important steps of this process.”


To come up and create a functioning business model, he believes that these factors need to be taken into consideration:

  1. What business sector does the company represent?

  2. What business model and strategy are being used by the company and its key competitors?

  3. What factors of external environment have a significant impact on business success, makes threats and opportunities?

  4. What factors of internal potential are determining the market position and creating Business Value?

  5. What a key company’s business processes are doing now and which data and IT systems company using for information supporting of decision-making process?

When all these factors are considered, Vitaliy believes that “On this stage we identify global configuration of the future business model, set requested functional areas. However, the common process of the creation data model may be equal in each case, but model for each company will have to be specifically tailored to the nature of each business and use case”.


Life As A Data Analyst Working On Multiple Projects


In recounting his experience working as data analyst, Vitaliy shared that “With more than 12 Years of experience in commercial & business analytics, and within companies representing different business systems and strategies (FMCG, Retail, Telecom, B2B, IT). Becoming a successful data analyst has required me to learn to integrate multiple data sources (Market, Sales, Product, Promo, Price) in one DWH to create a clear and insightful Business Data ecosystem.”


Important Lessons From Creating Impactful Data Models


The most important things lessons Vitaliy has learned through the working experience at Reiz Tech is that no 2 business are ever the same and that creating a tailored data model for each business entails a degree of understanding for the nature of each business. His example:


“For Lithuanian-Ukrainian retail Novus it was about creating a commercial analytics tool and report: which covers operational analytics for directors; business margin and turnover analytics for top managers; product and purchasing policy analytics for category management; campaign and CRM campaign analytics for trade marketing and loyalty departments.“


In his opinion, a data model that can help make a company succeed and improve efficiency requires, “the development of methodological models for analytics, as an example – creation multi-factor segmentation and assessment of the customer base potential; methodology for assessing efficiency of Promo activities and calculating lost sales; model for identifying sources of growth and business risks; model for customer base mobility and forecasting potential customer churn (customer sustainability risks)”.


Creating a working business data model needs understanding the questions that’s necessary to be answered. Once you have figured that out, you can construct a pathway with objectives that can mold your data model to help your business capitalize on its potential and make the best decisions possible.





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