Why investing into big data analytics is not enough

Big data analytics has achieved wide acceptance as a means to generate insights into what clients want and to derive good, evidence-based business decisions from it. However, while confidence in analytic decision-making is growing, there is also evidence to suggest that investments into big data analytics are not yet paying off. In fact, according to a report from the Economist Intelligence Unit (EIU) in collaboration with ZS, a sales and marketing consultancy, only 2% of respondents indicated that their analytics efforts had a “broad, positive impact” on business.

Bringing technologies together

But what do this low success rate of big data analytics in business mean? According to the respondents (consisting of sales and marketing as well as data science executives from companies with an annual revenue of more than USD 500mn), the issue is not about owning the right technology. In fact, the study indicates that 90% of respondents said they have already implemented a cloud-based big data infrastructure or are planning to. Over half were already investing heavily into analytics technology.

While technological capabilities have been scaled up, establishing a link to business and business strategy will take more time. One main obstacle can be found at the back end. Over half of the surveyed companies are still struggling to connect platforms with data capabilities. For this to succeed, a truly integrated platform is required, i.e. one where data from legacy systems such as ERP or CRM is linked to new sources of data. In fact, the study reports that data aggregation and integration is one of the biggest challenges that companies face. Often fragmented over different mainframe computers and legacy systems, data still has to be extracted from different data silos and cleaned before it can be properly analyzed. The result is that decision makers often do not have the data they need to act upon, simply because they cannot access it yet.

Hiring data scientists not enough

Another obstacle is at the front-end where analytics results are supposed to be translated into meaningful insights. As the study points out, companies are facing major difficulties in breaking down information silos between departments. Apparently, communication between professionals that work directly with data analytics and those responsible for business performance is still wanting. The problem is that data professionals often still lack a true understanding of the business a company operates in and its value chain. On the other hand, the transition to an organization embracing an “analytical culture” has proven to be difficult. Therefore turning big data analytics into action that creates business value will require closer partnerships between data science professionals and their business partners and the willingness “to embed analytics processes more closely into the fabric of the business”.

The study also addresses outsourcing of analytics capabilities to an external vendor, as almost all surveyed companies outsource their analytics-related processes to some extent (only 4% of respondents do not). Outsourcing is not only a good way of closing an internal knowledge and skill gap; it also helps companies take advantage of latest technologies, especially ones that are too expensive to build and run in-house.

The study concludes that strategic relationships with a few trusted suppliers are the most promising – provided the supplier acquires the domain expertise needed and is, in turn, also willing to “share their learnings with the client, ensuring that the internal teams are consistently apprised of outcomes and best practices”. The survey shows that 52% of the surveyed companies are seeking to adopt strategic partnerships with lead suppliers, a number that is due to increase in future.

Personalization is expected

There is no doubt about the potential business value of analyzing large data sets in understanding and engaging clients or identifying new business opportunities. In-depth knowledge of customer preferences is key to business success nowadays. Retail has taken the lead where analytics have been commonplace for a while now. Since many consumer retail businesses have been using big data analytics for years, people are expecting a certain degree of personalization, which, in turn, depends on how well you know your clients and understand what they expect and how you can meet their expectations. Improving the client experience in this manner “must be linked with real-time interaction”, as the report states. But as with many types of technology-driven changes, it might take more time for businesses to adapt. The quicker they do, the more competitive edge they can gain out of the process.

Adviscent has long-standing experience with and rich domain knowledge of the financial services industry. To learn how your business can set up an environment that enables a personalized client experience and real-time interaction along the client journey, contact Thomas Bosshard.