A Closer Look at Transformation: Descriptive to Prescriptive


Next up in this transformation series is the eighth enabler: the evolution from descriptive to predictive analytics. At the heart of future success lies the ability to leverage insight for competitive advantage. Yet, analytic capability and data driven cultures are lacking in most organizations, and most executives when assessing their positioning on a descriptive-to-prescriptive scale answer level one. The table below defines each level:

Analytic Levels

So most companies still use current methods such as traditional business intelligence (BI) to focus on reporting and analysis that seeks to answer questions related to past events – what happened (level one). In levels two and three, advanced analytics is used to answer questions such as: why is this happening, what if these trends continue, what will happen next (predict), and what is the best that can happen (prescribe). To accomplish this, analytic initiatives need to leverage an insight-action-outcome framework that starts by defining outcome-enabling insight and ends with a focus on data provisioning.

Blog - Descriptve to Prescriptive

A recent MIT Sloan Report effectively uses a maturity model to describe how organizations typically evolve to this state of analytic excellence. The authors through their analysis of survey results have created three levels of analytic capabilities:

Analytic Maturity Model

The evolution through this maturity model moves a company’s focus from hindsight to insight to foresight. Companies of all sizes will find this evolution unavoidable, driven by many of the factors described in a previous post on Insight. By way of summary, here are the drivers behind the descriptive-to-prescriptive enabler:

  • Most disruptive technologies likely to impact the enterprise have data at its core – there is no denying that digitization is altering the data landscape. From the explosion of user generated content, to the emergence of sensor-driven data, the tsunami of the past decade is sure to intensify. This data explosion represents a great opportunity to leverage unprecedented levels of insight – but it creates considerable risk, as most companies are not prepared to exploit this new insight. This brings us to the second driver
  • The overwhelming availability of insight shifts competitive advantage to those that harness it – companies that cannot exploit this data phenomenon risk competitive disadvantage to those that can. Insight and experience are the new battle grounds for differentiation and this places a premium on analytic excellence. The future belongs to those that can mimic the data and analytic prowess of Internet companies
  • The digitization of virtually everything creates data across a broad range of industries – as described in the first driver above; the data growth we experience today will intensify as digitization expands. Across industries, many companies will view existing and new sources of data as an asset that can be monetized. Third party data will therefore become a bigger piece of the insight equation, driving companies to find ways to leverage it
  • The Enterprise is not prepared for a future driven by insight – analytic excellence is critical to success in that future, and most companies lack both the analytic excellence and data-driven cultures required to succeed. This driver puts analytics at the center of future transformation initiatives and compels the enterprise to evolve up the analytic maturity curve
  • Actionable insight is growing more critical to everything – insight will be increasingly viewed as an enabler for: growth, smarter decision making, next generation experiences as we move towards personalization, next generation efficiency as we seek new ways to optimize, and a level of effectiveness never before seen in and across enterprises

The drive towards analytic excellence puts the descriptive-to-prescriptive enabler at the heart of the transformation program. But there will be many challenges, as I view this enabler as one of the most difficult to achieve. For one, the availability of strong business-focused analytical talent is the greatest constraint to analytic excellence. Additionally, the point of failure in many cases will occur in the process of converting data to insight, insight to action, and action to intended outcomes. With this in mind, some of the tactics associated with this enabler are:

  • Develop a road map for analytic excellence – start with a focus on major opportunities and put a plan in place that moves the organization from the current level of analytic maturity (mostly descriptive) to the highest level of maturity (prescriptive). The MIT-Sloan model is a great place to start. Define the metrics required to assess progress as the evolution to the future state plays out. The road map should ensure that data management evolves to support the enterprise needs. In aspirational companies, the ability to capture, analyze and share insight is limited. As maturation occurs, the data management function must strengthen to better enable these capabilities. In the end state, a mature analytics capability uses insight to make decisions, guide future strategies, and guide day-to-day operations. To accomplish this, the road map must expand the advanced analytics portfolio (visualization, predictive analytics, text analytics, voice analytics, etc.).
  • Embed analytics into operations – historically, business intelligence technology has been used to report on operations. This paradigm must shift as part of this transformation initiative to enable operations through analytics. This embedding of analytics into operations is the ultimate goal, in affect enabling both efficiency and effectiveness. Understanding the current environment is the starting point. From there, value creating insight should be identified and enabled through the right analytic methods. Lastly, the insight must be embedded into operations using various approaches like analytic applications that create automated closed-loop systems
  • Start with business outcomes – the traditional approach tends to start with data and quickly gets lost in data management activities. The goal is to enable the business through insight, so it starts with outcomes, moves to the insight and actions required to enable those outcomes and then focuses on insight and action-enabling data. The data tsunami in front of us makes it impractical to start with data and drives a critical need to narrow the focus
  • Establish Analytic Centers of Excellence – leading organizations are creating a centralized analytics unit that makes it possible to share analytic resources efficiently and effectively. These centralized units are the primary source of analytics, providing a home for more advanced skills within the organization. This same dynamic could lead to the appointment of Chief Analytics Officers (CAO) in the future. One of the key focus areas will be advanced analytics skill acquisition and development
  • Instill a data-driven culture – the shift from gut-based decision making to insight-enabled decision making could be the toughest hurdle. Barriers include management and culture rather than data and technology, and the business must overcome a lack of understanding of analytics and its ability to improve the business. One effective strategy to overcoming cultural challenges is to focus on some of the biggest business issues. The importance of solving those issues ensures executive sponsorship and has proven to move some of the biggest cultural hurdles
  • Leverage new techniques and approaches – to inform decisions and actions, new techniques and approaches should be embraced. For example: a) create an experimentation culture and environment and use advanced analytics on massive data sets b) combine traditional reporting with forward looking indicators based on predictive analytics c) drive optimal decisions and actions using simulation d) deliver insight and actions through automated closed-loop systems that effectively embed insight into operations e) move to iterative planning cycles fueled by insight f) begin to deploy sophisticated modeling and visualization tools
  • Start the evolution with focus and then expand – evolving from the descriptive to the prescriptive stage is a journey. The previously mentioned MIT Sloan Report provides a number of great examples of how the journey has played out in transformed organizations. Focus at the onset is critical and expansion almost follows a natural course if done correctly

That’s a look at the eighth enabler. For a review of this transformation series to date, here are the links to each of the prior posts:

Forcing Functions:

Enablers:

6 thoughts on “A Closer Look at Transformation: Descriptive to Prescriptive

  1. […] most companies still use current methods such as traditional business intelligence (BI) to focus on reporting and analysis that seeks to answer questions related to past events – what happened (level one). In levels two and three, advanced analytics is used to answer questions such as: why is this happening, what if these trends continue, what will happen next (predict), and what is the best that can happen (prescribe). To accomplish this, analytic initiatives need to leverage an insight-action-outcome framework that starts by defining outcome-enabling insight and ends with a focus on data provisioning..  […]

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