Posted on 2020-09-01
In the current business environment, no business decision can be made and executed if it is not supported by data. Almost all company decisions are backed by hard numbers. In the past, recommendations from industry experts is often enough to make strategic decisions, but nowadays, any recommendations, even from veteran industry experts cannot hold ground if it’s not supported by cold numbers.
Data – this is what defines the present business arena. And those who knows how to use it will have the strategic advantage in dominating the market and edging out their competitors.
Data Analytics offers promising benefits to companies. Some specific use of analytics is in forecasting sales, predict customer churns and in detecting fraud. With these problems being solved, a company can increase its profitability and reduce operational costs. Also, thru data analytics you can unlock and discover the behavioral patterns of consumers.
With this, current products can be improved to better cater to customers’ needs and, in some cases, these consumer insights lead to the development of new products. But adapting analytics as a part of an organizations’ business strategy can be challenging. In some cases, organizational restructuring is needed, hence it is not easy for companies to execute and adapt it. IT infrastructure investment is also needed in order to meet the future demand for data storage and computing resource needed for analytic solutions deployment. These are some of the challenges companies’ face in order to transform into an intelligent and adaptable enterprise in the fast-moving world.
For businesses to successfully transform into a data-driven enterprise, crafting an analytics roadmap is necessary. A plan of action that outlines the goals & strategies, and how analytics is positioned or embedded at each area and phase gives a good picture of the direction in terms of the analytics plan execution in the future. The enterprise analytics-transformation plan are mostly long term which can take a couple of years in order to reach a "data-maturity" level where the company and its employees are properly maximizing the value of the data, and what it can potentially give the enterprise in terms of growth.
Building an analytics team is crucial in adapting analytics as a part of business strategy. A good analytics team is usually composed of data scientists, data engineers and data visualization developers. These teams will handle the critical analytics and data-related projects of the company across departments from sales, operation, marketing, human resource and any other departments that have available data. Hence, collaboration across departments is necessary for a successful analytics project.
Taking this step for companies and successfully integrating analytics in their business strategies, goals & plans and even company culture is not an easy task. This involves effort and investment cost. Most importantly, company leaders and executives should rally behind these analytics initiatives for it to have continuity and successful adaptation within the enterprise.