Big Data and Analytics Helps Business Transform and Gain Competitive Advantage

In our benchmark research on business technology innovation, organizations ranked analytics the number-one priority (for 39%) among six technology trends. Big data, perhaps because it is a more technical concept, ranked fifth, with 11 percent of organizations calling it a top innovation priority. But in this time of global business, nonstop communications and fierce competition, more organizations are finding that big data and analytics together can help them cope with constant change. They can help organizations face imperatives such as increasing time-to-value and becoming more agile and adaptive.

Using them properly can increase the potential for competitive advantage in business in various ways. For example, customer analytics, on which we have conducted benchmark research, is changing with access to new sources of information.  In telecommunications, for vr_bti_br_technology_innovation_prioritiesinstance, call detail records, which contain enormous amounts of data, are being sifted and combined with customer records to determine things such as quality of service at the individual level. These sources can be combined with various others to help determine a customer’s value and propensity to churn. Machine learning can then be applied to figure out the best action to prevent churn at a cost commensurate with the value of the customer. Attribution modeling is changing as well with big data, especially since online tracking is no longer dominated by cookies and users need to join online and offline channels. Human capital analytics, on which we recently released new benchmark research, is a hot area as well. It includes analytics in the area of talent management, which is concerned with salaried (rather than hourly) employees and hard-to-find skill sets. It focuses on things like recruitment, career paths, retention and the entire employment life cycle (often beyond a single organization). Human capital analytics also includes workforce management, which uses big data and analytics to optimize scheduling and resource allocation and to match skills to particular tasks. Another area, operational analytics, often focuses on efficiency and can have a great impact on customer service through improvements in delivery times, system responsiveness or predicting outages before they occur. In the area of governance, risk and compliance, things such as fraud detection and system and network management, cybersecurity and portfolio risk all can be addressed using some form of big data and analytics.

New information and new technology are impacting almost every industry and every function, but in different ways and at different speeds. For instance, healthcare and banking are driven more than others by risk management and regulatory compliance, whereas services such as retail are driven more by sales, and manufacturing is driven more by efficiency and cost containment. Retail is facing a great deal of discontinuity as e-commerce, and in particular Amazon, have forced established retailers to completely rethink their strategies.

This discussion is not complete without addressing the impact of big data and analytics on organizations’ people, processes and culture, which is a particular topic of my current research. We are seeing in all our research more empowerment of business users and business analysts. This is being driven by a number of internal factors such as industry competition but also by the ability of business users to rent applications from the cloud without incurring significant capital expenses and being dependent on their IT groups. Until recently IT chose new tools and provisioned a company standard, but that is less true today. From an information and insights perspective, now the power lies with who owns the analytic agenda rather than the technology agenda, and the analytic agenda is owned by those with the most business savvy. At the same time, we see a people challenge, which according to our big data benchmark research,vr_bigdata_obstacles_to_big_data_analytics %282%29 manifests in the top two challenges to big data analytics: staffing (for 79% of organizations) and training (for 77%). Organizations have trouble finding qualified professionals to manage big data and providing training to those already on board. The finance department and other numbers-oriented functions, which have much of the analytic talent, are starting to exert influence in less analytically savvy parts of the organization such as human resources and marketing. Since having a complete view of the customer is vital today, the marketing organization is a candidate for big data initiatives, but only if the team is analytically savvy and can take advantage of new sources of information and new technologies. Otherwise, Finance or IT will ultimately lead the new analytic efforts of the organization.

There is an interesting dynamic occurring here. Finance and IT are natural allies in that both are numbers- and tools-oriented. Since the 1990s they have led adoption and use of enterprise technologies such as ERP and business intelligence. The marketing department has had a different orientation, but the strength of marketing is its ability to produce top-line revenue by understanding and influencing the customer. I expect that when these forces come together and cross-pollinate the organization, we will start to see a real transformation in 21st century business.

As with many other trends, in their enthusiasm people offer big data as the answer to every question. I heard a great one-liner the other day: “If you are a bartender running low on gin, there’s no need to worry about big data.” I like this because it refocuses the discussion to look first at the business problems organizations are trying to solve and the data later. I’ve written a few pieces trying to add some structure to how to think about big data analytics such as four pillars of big data analytics and moving from the technologically oriented big data Vs to the business focused Ws. As well my colleague Mark Smith has written about four types of discovery technology, an area that is critical to exploring the data that is becoming so abundant. Hopefully, these frameworks and approaches can help companies think through the challenges around big data and analytics and the transformation that ensues.


Ventana Research

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