By Pedro Ramalhete,
Nowadays data is a hot topic, everybody talks about data and how data is a pilar on the digital economy going forward. Digital companies are implementing machine learning and other advanced algorithms, however, is not rare to find companies without a clear data strategy. The lack of a data strategy early in the company journey can impact substantially growth and revenue by originating a opinion driven culture instead of a data driven culture. Let´s look two service companies in competitive markets.
Let´s look at company A, the company has millions of customers and needs to use data across the different business areas. The company is data driven, and, therefore, from early stages has built a large data warehouse where data from different operational systems is host (sales / costumer usage / billing etc). The data is collected, cleaned and structured and each department can access it using tools like SAS or SQL. Data is used for different purposes across areas, in marketing is used, for instance, for budgeting, pricing decisions, track customer revenue and churn, in BI, for data modelling and build business dashboards, in finance to track bad debt, build customer scoring models and test financial assumptions, in sales, to track customer acquisition per sales channel across regions and so on.
Every month the company closes it´s numbers and has a historic of past decisions so the data is the same independently who analyses it. The company is working on a data lake to host raw and unstructured data. Discussions at company A are about best ways of using data, data insights for each business area and customer, product and channel decisons using data as input.
Now let´s look company B, the company is not data driven consequently having all the data structured and widely available is not a priority. Company B, in early stages of company life cycle didn´t have a clear data strategy. The data is in silos so each department works on his own data that is not widely shared. The company doesn´t have a process to close all monthly numbers and therefore the impact of historic actions could change which means key numbers could differ affecting the quality of analysis. The company has a talented team in the BI department but lacks quality data and resources to deliver to all internal requests. So there are many meetings mostly unproductive with different confronting opinions. In most meetings there is no data, or data presented has different origins with discussions around if data is correct or which one is the best data source.
So how do you think could revenue and profit be impacted? by a lot, the probability of company A maintain his profitability and growth is high compared with company B which, in a competitive market, has high probability of a constant decrease in profitability.
The available numbers presented to the board to back a decision are completely different regarding the two companies .
In Company A meetings are much more productive, each area can cross data, build its own analysis, discuss it and build consensus. There is no incentive to hide numbers, quality numbers are presented to the board and it can quickly and effectively respond to any change in market conditions or consumer patterns.
The same will not apply to company B. In company B the probability of wrong decisions is high and many wrong decisions can influence decisively future profits.
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