In the past few years we have not seen many big data analytics projects launched in our market, not even among the market leaders of mass customer industries. At the same time there are several examples and case studies from global markets, proving that companies who have embarked on big data solutions, managed to gain significant business benefits.

We at Stratis are always trying to convey a framework of thinking to decision makers that approaches big data – often described in technical terms – from the direction of business advantages, leading to building a big data roadmap that follows the unique characteristics of our client.

Big Data: Creation of Strategic Value

The basis of our approach is the ‘value discipline model’ created by Treacy and Wiersema, suggesting that in order to achieve sustainable business success, companies must be competent in all three disciplines (as shown by the minimum threshold in the diagram) and excel in just one:

  • operational excellence – low or lowest price;
  • product leadership – the best product in the market, highly valued by customers;
  • customer intimacy – obsessive about understanding and serving customers.

Big data’s different arms fit well with the disciplines defined in the model.

It is also important to align big data projects with corporate strategy because without well-defined business benefits, big data efforts can easily become self-justifying and there might not be anyone on the business side interested in the ideas of IT.

Big Data: Products Based on Data or Analytics Capabilities

Regarding any competitive factors, a company may secure competitive advantages based on data: either by developing and marketing data-based products (product functions) or by creating added value from data analytics, therefore providing better, wider and more precise data analytics capabilities to its leaders.

1. Data-based products (product functions): the product’s key advantage lies in data utilization. An everyday example is the kind of navigation software that informs drivers about the actual traffic (based on data transmitted by the devices of all other users). Of course there are more fashionable themes, such as wearable devices (smart watches, fitness armbands) or software optimizing company operations based on data collected by sensors / users (e.g. marketing optimization, production optimization, precision agriculture). The essence is always the same: operation based on data analytics lies in the core of the product.

2. Analytics capabilities: big data analytics are those advanced methods that have at least two of the following characteristics:

  • predictive,
  • capable of analysing data flows,
  • capable of analysing huge volumes of data fast (in a reasonable time),
  • capable of analysing badly structured or unstructured data,
  • examine a vast array of viewpoints,
  • use advanced mathematical and statistical tools,
  • aim for uncovering hidden patterns / unknown correlations.

Big Data: Technology Choice

Finally, it is important to tell that there are a large number and variety of big data technologies available and the choice must be tailored to the business challenge at hand. Our consultants can help clients in making these decisions in order to complete their business development projects avoiding by-passes and losses.