Why is enterprise adoption of BI tools low?

In this age of digital transformation, when organisations claim data to be the new currency, one would expect the rampant adoption of business intelligence (BI)/ data analytics tools. Counter-intuitively though, these tools are far from being ubiquitous; despite several rosy projections for the market for BI tools, actual adoption is seen to be less than 35%. Let’s understand why the near-unlimited potential of BI tools is yet to be embraced widely.

Challenge#1

Inadequate clarity on business outcomes from data analytics

Market research firm Gartner, in its evaluation of the low data analytics maturity of organisations, highlights that the use of data is rarely linked to clear business outcomes. Leading management consultancy McKinsey has also alluded to the same problem in some of its work on the topic.

Getting a well-defined answer to “What do you want the data to do for you?” should be the starting point for companies embarking on their data-driven journey. It should guide them in determining what data to source, what to use, how to integrate all of it and derive meaningful insights with the help of an appropriate BI tool. Without that, it is easy for companies to not only get drowned in data or be afflicted by ‘analysis paralysis’, but also adopt tools that are simply not fit-for-purpose.

Lag between strategy and culture

Challenge#2 

Lag between strategy and culture

As with most change management endeavours, a transformation in culture is a critical success factor. Management experts are almost unanimous in their view that changing a mindset and establishing a new organisational culture– in this context, the adoption and use of data-driven intelligence and insights for making business decisions- is a serious challenge.

So, while many enterprises recognise the strategic imperative of becoming a data-driven organisation, they fail to invest the management bandwidth to drive the requisite cultural transformation. The result?  A sub-optimal ROI on their investments in BI tools. The impact this has on both continued and future adoption isn’t hard to fathom.

gaps in structure and infrastructure

Challenge#3 

Gaps in structure & infrastructure

The structure issue is closely related to culture as mentioned above. Traditionally, IT departments owned and controlled ‘data’ and ‘business intelligence’ exclusively. Enterprise silos limited adequate and meaningful collaboration between IT and business stakeholders.

The implosion of data due to the digital revolution in the past two decades has broken down many of these silos. Despite the relative improvement in the flow of business intelligence, the complete ‘democratization’ of data has still some way to go (more about that in an upcoming post).

Another impediment to the widespread adoption of BI tools is the burden of legacy IT systems and infrastructure that enterprises grapple with. Tools and platforms that are interoperable with existing IT infrastructure or require minimal changes stand to be more easily adopted.

Deficient data interpretation skills

Challenge#4

Deficient data interpretation skills

Having a data-driven mindset is one thing, the ability to interpret data is another. It demands a different level of skills to be able to take actionable insights from the stories the data tells. The better BI tools obviously do the heavy lifting when it comes to extracting the ‘intelligence’ and modern data analytics platforms do a great job with data visualisation, making it a lot easier for the user to make sense of it.

Truth be told, ‘data literacy’ remains a big problem in many organisations. There is a dearth of professionals with the skills that will enable them to capitalise on insights from data. Even as BI software vendors and analytics platforms use artificial intelligence and machine learning to provide insights on a platter to organisations, the skills deficiency does present a hurdle to greater acceptance of BI software.

The cost versus value conflict

Challenge#5

The cost versus value conflict

ROI on a BI software investment will always be a key consideration for the senior management of any organisation. Historically, enterprise BI software implementations have involved very high upfront investments together with reasonably high ongoing costs, making the total cost of ownership prohibitive for many enterprises.

Couple this with unsatisfactory results due to many of the reasons already discussed above, and the equation is loaded against adoption of BI.

However, the emergence of cloud-based analytics platforms and creative pricing models make these more affordable and cost-effective. At the same time, BI software vendors are going to great lengths to make their products more user-friendly and address increasingly complex intelligence requirements. We expect that this will allow enterprises to demonstrate greater ROI, justify their BI investments and lead to increased adoption.

Shift from reporting to recommending

Shift from reporting to recommending

Here’s what we know for certain now: the amount of data that any organisation generates will continue to grow exponentially. Data has the potential to be a key differentiating asset, provided its potential is strategically harnessed.

BI and analytics platforms cannot be limited to generating reports for management. They are increasingly expected to not only provide real-time and predictive intelligence for businesses, but also offer data-driven recommendations. Simply put, they should play a pivotal role in driving the business forward. For analytics companies like us that are focused on doing just that, these are exciting times.

The question is not if adoption of BI tools will rise, but when.