Nowadays, Data has become a strategic asset that companies could use to enhance their decision-making process and gain a substantial competitive advantage.

But just owning data is not sufficient: you have to be able to extract value from it.

In order to do that, there are several passages that you must complete that will make you use your data effectively. Information is often stored in databases that are not talking to each other, affected by inconsistency problems and complex logics, which drag down data manipulation and visualization issues. Moreover, keeping your data “dirty” after the extraction from the operational source – as your ERP, CRM or website – may make your data useless due to intrinsic poor quality, and require time-consuming procedures before having the numbers ready to be crunched.

If you want to effectively take advantage of your data, you need to think about the long run. Many firms make the following mistake: they excessively focus on current problems and needs and hesitate to develop a long-term data strategy. Rapid solutions, which help tackling current problems, may eventually reveal to be inappropriate or even counter-productive in the following years. For example, investing in a state-of-the-art reporting tool may lead to immediate benefits, allowing rapid key data monitoring and visualization and boosting performance, but lacking a solid architecture behind the scenes may eventually make our tool useless, whenever business logics or market conditions change. In such circumstances, modifying the way you manage your data directly in the front-end will be really difficult and expensive.

For these reasons it is important to develop a long-term strategy, promoting a “Data Culture” across the entire company, and actively engaging ITs, business analysts and executives.

According to the model proposed by Gartner (see picture below), the ideal data strategy should start from simple Descriptive analysis (What happened?), to proceed to Diagnostic analysis (Why did it happen?) and eventually get to make inferences (What will happen? How can we make it happen?) through Predictive and Prescriptive Analytics.

By moving in the right direction and climbing the analytics ladder, data enhances its potential and leads to competitive advantage and substantial profits.

So what are the fundamental steps to build an effective Data Strategy, which would make us able to get foresight and achieve our strategic goals?

 

  • Understand Key Data

 

In the age of Big Data, we can measure countless parameters and variables, stockpiling a huge amount of data. In this sea of information, what are the crucial data necessary to increase the value of our business? On which metrics is important to focus to achieve strategic and tactical goals?

The first step for an effective Data Strategy is to find the KPIs which best answer these questions.

 

  • Understand Data Sources

The subsequent step after finding your key data is to understand where the data is located. In order to do that, we must examine accurately our data sources, understand how data is produced and, above all, find out where data exists. Generally speaking, it is unlikely that the whole enterprise data is located in a single place, accessible by different business areas without particular procedures. Before getting to the executives’ desks, data may undergo different transformations, in a process that, without the proper architectures, could be really expensive in terms of time and resources. At the same time, it is important to understand the level at which Data should be recorded and imported (Do I need not just the date but also the time of a specific event?) and the frequency of updates. Similarly, it is necessary to ensure the preservation of historical depth in order to analyze the evolution of specific metrics.

  • Identify the best solution for your Data

In order to manage enterprise data we can employ solutions that differ in structure, key features and approach. Then, identifying which architecture best fits our needs may not be an easy task. It is better to build a Data Warehouse or to rely on a Data Lake? That depends, besides the characteristics of our data, on our necessities and resources. We should also ask how our data infrastructures will be managed after implementation.

Here are some example of questions that you may ask before getting your data solution.

  • Is the IT staff able to perform the necessary maintenance of our system?
  • Do our data sources provide complete information or we should add external sources?
  • It is better to build a Data Warehouse or to rely on a Data Lake?
  • Do our “raw” data need several transformations?
  • After being cleaned, are my data available to data scientists for Predictive Analytics and Machine Learning?
  • Who will produce my Business Intelligence reports?
  • Do I need static or interactive reports?
  • Who will read and use the reports? They will be visualized also by third parties?

These initial 3 steps allow us to outline a solid data strategy for our firm, taking into account our specific needs.

Eventually, we must remember that manage our data effectively is not an easy job. The entire strategy should be carefully planned and involve as many professional figures as possible. In this regard, adopting an iterative approach, aimed at preserving maximum data quality and involving final users can be the best choice.

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