Part six wraps up our Digital Enterprise road map series with a focus on moving insight delivery from descriptive to prescriptive. Throughout this series, I have stressed the importance of analytic excellence to long term success. But current methods such as traditional business intelligence (BI) focus on reporting and analysis that seeks to answer questions related to past events – what happened. Advanced analytics seeks 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). There is a growing view that prescribing outcomes is the ultimate role of analytics. 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.

However, the data side of this framework is growing in complexity. The digitization of virtually everything now creates data across a broad range of industries. Data is flowing through social media, medical and scientific devices, sensors, monitors, detectors, supply chain devices, instrumented cars, roads, domestic appliances, and much more. The utility sector provides a great example of this data tsunami and the growing need for analytics. The smart grid and the gradual installation of intelligent endpoints, smart meters and other devices will generate volumes of data. Smart grid utilities are evolving into brokers of information, and this type of information broker will emerge across all industries to feed a growing need to leverage third party data. For Utilities, this is a formidable IT challenge, but it is also a huge opportunity to move beyond simple meter-to-cash functions and into real-time optimization of their operations.

With insight and enabling data defined, advanced analytic technology drives the framework with levels of sophistication, speed and accuracy previously unachievable – but analytic capability and a data culture are lacking in most organizations. To address this, Holistic Strategies must incorporate a road map for analytic excellence that moves organizations from their current level of analytic maturity (mostly descriptive) to the highest level of maturity (prescriptive). Companies that effectively manage this transition will do so across these three levels:

In conversation with executives, most believe they are somewhere between level one and level two on the maturity curve. They understand that success in the future dictates an aggressive move towards level three, but the challenges can be overwhelming and the transition is not an easy one. At its core, this transition requires a shift from gut-based decisions to a data-driven culture that allows insight to guide decision making and actions. 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:

The report found that as the value of analytics grows, organizations are likely to seek a wider range of capabilities – and a more advanced use of existing ones. This dynamic is leading some organizations to create 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.

The availability of strong business-focused analytical talent will be the greatest constraint on the path to analytic excellence. Companies will explore hiring, training, and outsourcing alternatives to address the need for specialized skills. Another obstacle is the data itself, which will increasingly lead organizations to establish enterprise data management functions led by Chief Data Officers that coordinate data across business units. The information management challenge will grow as millions of next-generation tech-savvy users leverage feeds and mash-ups to bring data together in a way that lets them answer their own questions. This gives rise to new challenges, including data security and governance.

Actionable insight could be the biggest determinant of future success, which makes the move to analytic excellence mission critical. It seemed only appropriate to wrap up the digital enterprise road map series with what is potentially the biggest piece of the future road map. To restate the premise of the series, the digital enterprise is synonymous with the future enterprise – and for the purposes of this series, I focused on the enterprise of 2020. Trying to keep pace with innovation is a challenge for every organization, as cycles accelerate and obsolescence is generally just around the corner. However Executives can be certain about one thing: to succeed in 2020, companies of all sizes must exhibit these key characteristics:

• Growth-oriented
• Relationship-based
• Flat
• Effective
• Insight and engagement-driven
• Experiential
• Open, agile and collaborative
• Responsive and adaptive
• Fast, iterative and experimental
• Powered by knowledge, creativity and ideas

To enable these characteristics, companies will need to focus on several key enablers. Through this series, we addressed six road map elements that represent several of those enablers. This is by no means an exhaustive list, but one that focuses on two critical areas of excellence required for success in 2020. The first area is relationship (collaboration) excellence supported by the key areas of experience, engagement, and value ecosystems. The second is insight (analytics) excellence supported by knowledge, collective intelligence, advanced analytics and effectively managing the data tsunami. In closing, here are the six road map elements that we reviewed:

1. Developing a holistic strategy
2. Creating experienced-based differentiation
3. Creating an integrated social ecosystem
4. Developing systems of engagement and integrating to systems of record
5. Enabling effectiveness
6. Moving insight delivery from descriptive to prescriptive