Navigate with ease
Are you adopting analytics in your organization? Watch out for these pitfalls, say Arindam Banerjee and Tanushri Banerjee, authors of Weaving Analytics for Effective Decision Making.
Gartner predicts that, through 2017, 60% of big data projects will fail to go beyond piloting and experimentation, and will be abandoned. 1 For an analytics project to truly deliver value, organizations should have a well-defined script in place.
Information (data-driven decision-making has been the differentiator for many successful businesses over decades. The need for precision and specificity in decision has influenced policy makers to adopt analytical processes to extract insights about markets, which can be inputs for better business decision-making. Traditionally, marketing research departments spearheaded the collection and analysis of sampled market data and shared inferences with the decision makers.
However, over time business measurement systems have improved significantly in certain domains; as a result some organizations are collecting market data and other pertinent data continuously and more ‘completely’. In the process, both the scale of databases and their processing have undergone significant changes and the discipline is now known as analytics.
The analytics and big data initiatives are slated to continue to grow at a rapid rate around the globe in the next few years. Studies by management consulting firms such as AT Kearney have projected a 10% CAGR of the analytics software industry over the next five years. With
pressures on businesses to show growth and profitability and increasing competition, the role of analytics and knowledge management services in general is bound to grow further.
While the analytics competency in emerging markets such as India is also projected to grow at a rapid pace and there are many conversations around the probable need for it in the corporate landscape, there are several hurdles that may hamper the extent and rate of adoption of this knowledge-generating process.
challenges in analytics adoption in Indian organizations
A recent study4 of ours across business organizations in India has revealed some significant areas of concern
regarding organizations’ ability to adopt analytics effectively in the near term. Though our sample was of a modest size (twenty executive interviews across multiple industry verticals and case studies across five organizations), we nevertheless found some pertinent issues for practitioners to review before embarking upon the analytics journey. They are:
■ non availability of comprehensive business data: A prerequisite for effective data analytics application is the availability of appropriate data. It may be structured or semi-structured (or unstructured), but it is important that coverage of the available data source should be ideally complete and the variety of information available is broad enough to support the major decision-making areas. Many of these conditions are not satisfied in the organizations that we have studied so far. A secondary concern is the unorganized state of data in many organizations, which makes it difficult to develop a systematic information plan to connect to decision making requirements.
■ internal data in multiple and incompatible formats: A second dimension of the complication for organizations with large-scale business data is their availability in varied formats. This causes significant problems of consolidation. For instance, in the banking and financial services sector in India, there has been rapid development in computerization and automation of operations in most large public sector institutions, in the past decade and a half. A consequence of this trend has been that recent bank transaction data is available in standardized electronic formats but, their integration (or lack of it) with back data available in legacy physical systems (read: paper formats) makes it difficult to apply most processing methods reliably.
■ technology alone has limited potential to create impact: Offshore operations based in India seem to have, to a
The need for precision and specificity in decision has influenced policy makers to adopt analytical processes to extract insights about markets, which can be inputs for better business decision-making.
lesser degree, the problem of (non) availability of data. There is also a large technological infrastructure in place that churns out information from the available and more organized databases. A possible spin off of the superior technology infrastructure in offshore analytics operations (a significant part of the current analytics competency in India) has been the notion that the most effective and rapid way to develop analytics competency is to invest in a technology platform. Nothing could be farther from the truth. Technology alone cannot substitute for lack of data or the unorganized state of business data.
It is best to address the issues related to availability of appropriate data before a technology platform can drive effective analytics. Additionally, there are a few other motivational dimensions that have hampered effective use of analytics. They are explained as follows:
01 dependency on heuristics for making decisions: Taking cognizance of some of the constraints listed above, many businesses remain steadfast on their reliance on heuristic business rules developed over long periods of experience. People-driven decisions have overridden attempts towards standardized procedures and processes. The common refrain heard in these organizations is that information, since it is not available or is incomplete, cannot substitute the virtues of age-old ‘gut feel’-based decision-making.
02 market growth hides the need for precision in decisions: The perceived ‘futility’ of developing the analytics practice is also fuelled by the notion of the ‘growing market syndrome’. Data scientists are supposed to extract business insights that act as necessary inputs for precise decision-making in a highly mature and well-penetrated market. They are supposed to provide directions and refine decisions to hone in on the ‘close to the perfect’ set of decisions for an environment. However, when the markets are in the growing phase, such extraction of precise insights from past transactions is not quite relevant. Hence, what is the need for investment in analytics, they ask.
how should investments be directed towards adopting analytics in India?
The findings of our study seem counterintuitive, especially given the level of optimism that is prevalent about analytics in the industry today. However, it is never too early to begin the analytics journey in any organization. Percolating the culture of using pertinent market and business information, no matter how incomplete it may be, and factoring the insights into decision-making is a worthwhile step towards building long-term capabilities in organizations, specifically when markets will get crowded with the entry of more suppliers and competitiveness increases in the near future.
How then should organizations in India chart out their analytics journey? First, it may be prudent to use experts to develop plans to build relevant business data marts in organizations with an eye towards their future use. Identification of key data, which may help in the analyses of various future business scenarios, their probable
Data scientists are supposed to extract business insights that act as necessary inputs for precise decision-making in a highly mature and well-penetrated market.
inter-relationships and decisions regarding investment in their meticulous collection are important elements of this planning process. This is more like working backwards from a desired outcome to trace out what may be the required sequence of developing a competency to achieve it.
Second, experienced analytics professionals can also serve as useful mentors to enable organizations to equip themselves with the right capabilities for the future by
■ educating decision-makers about the appropriate way(s) to use information-based insights into decision-making processes ■ training executives in the correct ways to process data to produce business insights
It is important to note that the qualities of the experienced analytics professional include (see figure 01)
■ a requisite knowledge of data bases and their structure
■ adequate understanding of processing methods and their output
■ a significant connect with the business decision-making
While this might seem like a formidable set of qualifications for an individual to possess, what is necessary to realize is that eventually a good cross pollination of these capabilities across a critical mass of employees is required for analytics to flourish in the organization. The more adept ones among them act as translators across various functions (data management, processing, and decisionmaking) since their ‘blended’ capabilities provide them the necessary ability to connect with all. Finally, the guiding principle to manage adoption should be to chart out the analytics journey in detail from ‘start to finish’ (as much as one can) before committing significant investments in technology and infrastructure.
Following this principle, experienced analytics professionals can help ensure prudent use of organizational resources and a timely investment in the analytics process. ■ 01 https://www.gartner.com/newsroom/id/3130017 02 http://www.forbes.com/sites/louiscolumbus/2014/06/24/roundup-ofanalytics-big-data-business-intelligence-forecasts-and-market-estimates-2014/ (Accessed on September 4, 2015, 0925 hrs (IST)) 03 Üsing Marketing Analytics to Drive Superior Growth”, Mckinsey Quarterly, June 2014. 04 “Determinants of Analytics Process Adoption in Emerging Economies: Perspectives form the Marketing Domain in India”, Vikalpa,42(2), April-June 2017.
Experienced analytics professionals can also serve as useful mentors to enable organizations to equip themselves with the right capabilities for the future.