I have been closely following and writing about trends in the business intelligence market over the past three years. During this time, I can’t say that we’ve seen any “radically new” trends, and I don’t expect any to emerge in the second half of 2017. What I have seen, so far in 2017, is the “evolution” of the dominant trends driving a dynamic BI market. The global business intelligence market is expected to reach USD 26.50 billion by 2021, growing at a CAGR of around 8.4% between 2016 and 2021 (Zion Market Research).
Here are the 5 BI trends predicted to most influence the BI market in 2017
1. Data Visualisation/Discovery – most important trend
Although the trend to Data Visualisation/Discovery is not new, it ranks as the most important business intelligence trend in 2017 by BARCS BI Trend Monitor. With the explosion of “big data,” this finding is not surprising. It is virtually impossible to decipher huge amounts of data from a wide variety of sources in static rows, charts, and tables. Data visualisation allows us to use our brains’ visual pattern recognition capabilities to digest and identify the most relevant information faster. BI users recognise the value of visualisation to identify hidden trends and patterns in data, to track business performance, to mitigate risk, and to seize new opportunities.
2. Self-Service BI – a high priority
According to Gartner’s 2016 Business Intelligence Magic Quadrant, BI has moved “past the tipping point of a more than 10- to 11-year transition away from IT-centric reporting platforms to modern BI and analytics platforms”. In fact, Gartner foresees that by 2020 self-service BI platforms will make up 80% of all enterprise reporting!
As we are all aware, “big data” has been growing at an exponential rate. Correspondingly, self-service analytics has been growing in direct proportion to the increase of data available to analyse. Business teams have been eager to seize the opportunity to use the data to improve business performance. They have been frustrated by dependence on busy, often understaffed IT teams for all their reporting needs. This has led business teams to clamor for modern self-service BI platforms that allow them to rapidly gain insights from data for a better understanding of their business and customers.
3. Self-Service Data Prep/Integration – rapidly emerging
While most trends in the BI market are relatively stable, there has been a surge of interest for self-service data integration for business users. We hear from many clients that data preparation is one of the most difficult and time-consuming challenges they face when using self-service BI and analytics tools. According to Rita Sallam, research vice president at Gartner, the industry has responded to this data challenge. “Data preparation capabilities are emerging and will provide business users and analysts the ability to extend the scope of self-service, enabling them to access, profile, prepare, integrate, curate, model and enrich data for analysis and consumption by BI and analytics platforms.” Similar to what self-service BI platforms have done for traditional IT-centric BI, Ms. Sallam predicts that self-service data integration will, “Reduce the significant time and complexity users face in preparing their data for analysis and shift much of the activity from IT to the business user to better support governed data discovery.”
The self-service trend in data integration is expected to become more and more mainstream forcing leading data integration vendors to offer options and interfaces as well as governance frameworks for data integration by business users. In addition, BI vendors are adding value to their analysis and data visualisation tools by extending their own business user data capabilities to include more-advanced data preparation features (BARC).
4. Cloud Data and Cloud Analytics – on the rise
In the past, many companies have been reluctant to share data in the cloud due to data security concerns. However, more and more organisations now consider data security to be a major benefit of transitioning to the cloud because cloud vendors often have more resources allocated to manage security than any single company has. It makes sense for business analytics to be in the cloud since a lot of sales, marketing and financial data already resides there. According to an industry report (IDC’s FutureScape), the use of cloud analytics will become more pervasive, and through 2020 spending on Cloud-Based Big Data and Analytics Technology is expected to grow 4.5x faster than spending for on-premises solutions.
76 percent of organisations in IDC’s survey cited speed to deployment as the top reason for going to the cloud. Other reasons were: the facilitation of collaboration internally as well as with partners and customers; no requirement for buying or configuring hardware; more scalable solution and licenses can be easily added as needed.
5. BI Governance – needs to “grow up”
When it comes to governance for BI, there seems to be a lot of room for improvement. As the responsibility for reporting becomes increasingly scattered among different individuals and departments, governance has become much more challenging. In the past, limited governance of self-service BI implementations has allowed better data access for business users but has sometimes led to data redundancy, data privacy and security breaches, and even instances of public disclosure inconsistencies. Self-service BI projects require a fine balance between flexible access to data and data governance for data discovery and distribution that results in an agile, data-driven culture.
Although BI analytics has increasingly been placed in the hands of the business user, it is paramount that IT remains involved in its governance. BI governance works best when IT and its business partners jointly define and implement an infrastructure that supports the BI strategy and enterprise goals. Governance infrastructure should address the distribution, complexity, flexibility, and cost of BI. An agreed data and BI tool governance framework is essential to avoid losing control over data.