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The Year Ahead: 2017 Predictions for Analytics

Adoption of analytics across the enterprise is occurring at breakneck speed, and there is no sign of slowing down. In 2017, we'll witness a critical point as organizations seek analytics that extract greater value from all available data.

• By Venkat Viswanathan
• January 17, 2017

Adoption of analytics across the enterprise is occurring at breakneck speed, and there is no sign of slowing down as we head into 2017. More business users are applying insights from analytics in their day-to-day roles in order to maximize the value of their data.

Applying analytics to unstructured data, specifically data from social media, mobile, the Internet of Things (IoT), and publicly available information, can reveal considerable insights. Yet analyzing data from these unstructured channels presents challenges for unprepared enterprises. Industries such as consumer product goods, banking and financial services, insurance, and energy and utilities are racing to invest in analytics that can help them strategize and position for competitive advantage.

The following are areas in which companies should expect analytics to evolve and change their industries in 2017.

Social Media Analysis Becomes a Company's Most Valuable Market Assessment

Analysis of social channels has become a significant factor in market research. This external data provides a unique understanding of consumer preferences by allowing organizations to witness the everyday conversations that surround their business. Advanced analytics with social data can be used not only to understand customers but to drive innovation and inform decision making. More traditional market research, such as surveys or focus groups, will become outmoded as the pace of business and flow of data increases.

IoT Places Analytics at the Forefront

Data from mobile devices -- such as mobile shopping behavior -- has been analyzed for years. Mobile is the largest provider of such consumer behavior, but it will be joined by many other channels, thanks to IoT. As we move ahead into 2017, organizations will need to layer analytics to gain insight from the substantial data that connected devices can provide, which can help them refine how their offerings are personalized for consumers.

Even as IoT is coming into clearer focus and forcing change, it is also bringing multiple challenges. IoT offers no existing communications standard, requires a new ecosystem separate from the Internet, raises questions of data privacy, and will expand on massive data lakes that can be difficult to analyze. Organizations with aspirations for more advanced, data-driven decision making will invest in analytics solutions that can help them prepare for IoT.

Content Delivery Becomes More Personalized

An increased need for personalized content will lead to greater demand for hypertargeting -- the delivery of content based on specific interests and behavior. Companies will need to get closer to their data to understand how to create content with greater relevance to a user. Like any relationship, the way a customer interacts with a brand can change over time. Analytics can help make the picture clearer, leading to more intelligent content delivered in the right channel to influence behavior.

Analytics Will Drive Product Innovation

Data analytics will completely revolutionize the way companies approach product innovation. It will be more consumer-informed, catering to existing, unmet needs. Typically, a company spends a lot of time and resources developing products within research and development teams, often in a vacuum. After 12 to 18 months, a product may be ready for market, but that market has been changing over that entire time.

The R&D teams should benefit from greater interaction with analytics from internal and external data resources that will allow them to understand the core challenges of the business. Increasingly, digital transformation will push product innovation and analytics closer so that they happen simultaneously.

Machine Learning Will Be the Future of Consumer Experience

Machine learning algorithms will begin to find applications in the area of customer experience management. With increased processing capability, these algorithms can find patterns in customer data. These patterns are now being used to provide specific offers and promotions to a certain subset of customers, resulting in optimized marketing spend.

Need for Unified View of Consumer Buying Journey

Understanding the consumer journey has consistently emerged as one of the top priorities for CMOs. Marketers will look for ways to get a more unified view of the consumer's buying journey and will strive to integrate the online and offline components to get a more accurate view. Unified customer journey maps help marketers increase engagement, improve the consumer experience, and optimize customer lifetime value, which results in a direct increase in revenues and profitability.

Emergence of On-Demand Analytics

More companies will actively engage partners for on-demand analytics. Using near real-time data enables more accurate decision making; enterprises will need actionable insight into complex real-time data from a multitude of disparate sources.

According to Gartner, "marketers' dependence on data and analytics is accelerating rapidly. Nearly 70 percent of marketers expect most of their marketing decisions to be quantitatively driven by 2017." Whether analytics are being applied to customer retention or visibility for a sales campaign, the quality of on-demand analytics for insight in near real time is critical.

Disruptions and Transformations

In addition, industries will have to face disruptions by digital, analytics-driven start-ups that sell directly to consumers via subscription models. As a result, large companies will have to prioritize digital transformation, and analytics will play a prominent role.

The automobile industry, for instance, has a very traditional need for analytics on information such as credit analysis. Their existing analytics capabilities are capable of meeting both traditional and new analytics needs, but the auto industry is one example of a sector that will increasingly engage third-party analytics partners in 2017 to help them understand the extensive amount of data necessary for digital transformation.

The necessity of data and analytics is only growing and the insights companies are gaining are becoming more invaluable. In 2017, we'll witness a critical point in the evolution of analytics as organizations move beyond analysis of inwardly focused corporate data and seek analytics that extract greater value from all available data.