10 Predictions For Analytic Decision Support by 2015

By 2015, companies will increasingly blend math, technology and decision sciences, employing user-generated data to infuse analyses with behavioral and social-network insights.
Prediction 7: Process Automation Will Take the Forefront

Certain areas of analytics, such as marketing mix modeling and trade promotion analytics, will move from the research/discovery phase to become mature, repeatable methodologies. These methodologies will be “operationalized” with automation technologies, enabling analytics at a greater level of detail and scale than previously possible. For example, we'll see marketing effectiveness analyzed by product, SKU, channel, country etc. However, each company will need to assess the applicability of the automation paradigm to its analytic challenges; there is a potential danger that pure automation will supersede necessary human intervention and oversight.

Prediction 8: Open Source Analytics and Analytics-as-a-Service Will Gain Adoption

Open Source analytics platforms will emerge to increase adoption and better mine the wisdom of crowds. Open source R has already emerged as a leading platform for statistical innovation and collaboration both in academic and industry circles. Adoption is evidenced by commercial vendors of R, such as Revolution Analytics, focusing on scaling the R computing language. There is also a trend of collaboration between proprietary and open source platforms. For example, Tibco Spotfire and SAS now offer the option to call R functions or scripts within their environments. This signifies a shift towards loosely coupled, open computing platforms.

Analytics-as-a-Service will be commonplace and will take different forms, including analytics services providers, analytics focused SaaS companies, existing IT services, system integration and data providers moving into value added analytics services. Outsourcing and global delivery will yield significant supply side benefits to the analytics industry.

Big players such as IBM and Accenture have already turned their attention to analytics. IBM, for example, has acquired SPSS and it opening analytics centers in India, China and elsewhere. There is an increasing trend of companies in the Fortune 500 issuing analytics RFPs. Analytic Software-as-a-Service models are being deployed in many areas, including Web analytics (Google Analytics and Adobe Omnitur), marketing analytics (M-factor), and hosted/on-demand business intelligence platforms (Panorama, SAP BusinessObjects On Demand, etc.).

Prediction 9: Behavioral Economics Will Gain Traction

Behavioral economics is increasingly being applied in the corporate world. While analytics helps us gain insights and make decisions, those decisions can create a positive impact only if human biases are taken into account during implementation. Analytics insights will challenge many traditional ways of working. Executives championing data-driven decision making will need to leverage an interdisciplinary approach using business + technology + mathematics + behavioral economics + social anthropology. As this becomes a formal practice in corporate operating procedures and corporate strategies, better understanding of human biases would help develop cognitive repairs or nudges to ensure better application of decisions.

Behavioral economics embodies principles that explain the workings of the brain. It helps in developing ways to influence behaviors. For example, CVS Caremark recently changed the choice architecture on an online renewal form to increase the rate at which patients renew their medicines. Behavioral economics concepts, such as choice architecture, can be combined with consumer insights gained through analytics and applied to targeted marketing campaigns, conversion rate optimization, portfolio optimization and so on.

Today, more and more organizations are investing in dedicated consumer insights teams. They focus on developing segmentation strategies, understanding customer lifetime value, market sizing and so on as independent initiatives. Over the next few years, companies will move toward a more holistic approach to understand their customers.

Prediction 10: Convergence and Collaboration Will Be the Rule

As new business models emerge and the boundaries of the value chain are blurred, a new era of convergence in the use of analytical techniques and frameworks will come into play. Cross-industry and cross-domain learning will lead to significant breakthroughs in the development and deployment of analytics solutions.

New business models are accelerating the need for convergence and collaboration. As an example, we've seen Microsoft enter retail, cellular phone networks enter the Netbook category, Dell moving from custom configurations to prebuilt offerings. Scottrade and Yahoo are collaborating on data and analytics to optimize lead generation for Scottrade.

Certain analytical techniques have historically been developed and used mostly in specific domains, with examples including yield optimization used by airlines, survival models used in Life Sciences, Lean principles used in manufacturing, and diversification used in finance. However, these techniques have a strong potential to be used across industries. For example, you might use yield optimization methodologies for online advertizing, survival modeling concepts for financial risk analysis and diversification for marketing and supply chain optimization.

Summing It Up

Analytics is going to play a key role for business in the coming years. In the wake of the economic crisis, the world is shifting to a "new normal," and we are seeing the advent of a new customer psyche. Institutionalizing analytics is not a destination or a goal for your company; rather it is a continuous process of internalizing and integrating analytics into the decision-making process. This will be a key differentiator for successful businesses and the route to both incremental and disruptive innovation.

Dhiraj Rajaram is the founder and CEO of Mu Sigma, an analytics services firm that helps companies institutionalize data-driven decision making. Before founding Mu Sigma, Rajaram was a strategy and operations consultant at Booz Allen Hamilton and PricewaterhouseCoopers. For more information, visit or write him at [email protected]