Business Analytics will continue to gain traction in every industry, and several key factors make this a foregone conclusion:
- The expanding universe of data and the opportunity and risk that it represents
- The growth of social channels
- The growth in mobile interaction and resulting need for analytics
- The critical need for customer intimacy
- The growing need for differentiation through innovation
- The rapid escalation of complexity that surrounds business today
- An increasing focus on value creation, growth and revenue generation
- The critical need for smarter decision making
- A continued increase in computing power that makes real time analytics viable
- The continued delivery of new and improved advanced analytic capabilities
- The movement to make advanced analytics software business user friendly
To truly address these factors and optimize the value obtained through analytics, companies have to evaluate traditional business intelligence environments and expand their analytic footprint. The focus is slowly shifting from reporting and dashboards that help us understand the past, to advanced analytics that use the past to predict and optimize the future. Analytic results will increasingly be used to drive operational execution and smarter decision making. As executives move towards analytics as a basis for competition, they will integrate analytics into company operations.
As this shift from traditional business intelligence to advanced analytics accelerates, a company’s ability to address several business and IT challenges will dictate success or failure. Some of these challenges include:
Analytic enablement – the environment for traditional reporting, warehouse and dashboard solutions worked well for its intended use. But advanced analytics are process intensive and work on large volumes of data to effectively deliver forward looking insight. As companies pursue the use of advanced analytics, they will look for architectures that better enable the delivery of this insight. This for example, could mean the creation of a customer hub that gathers data from all sources – cleanses and unifies it – and enables real time analysis to support customer-related processes. It could also mean a claims data hub that contains all the structured and unstructured data (email, adjuster notes, medical reports, police reports, etc.) associated with a claim. This would enable detailed analysis of fraud, claim frequency, claim severity and other claim analytics. Once the business objectives are defined, architectures will evolve to enable those objectives through analytics.
Data quality and data integration – the reliability of the insight provided by advanced analytics such as data mining, text mining, statistical analysis, predictive modeling, and others, is directly tied to the quality of data used to produce insight. Focus on data quality initiatives will accelerate as more companies look to benefit from advanced analytics.
A holistic view of important data (e.g. a single view of the customer) from across the enterprise is critical to providing accurate and timely insight. The inability to integrate and analyze data across organizational divisions, boundaries and systems is a major barrier to optimizing business outcomes through analytics.
A recent study by BusinessWeek Research Services of the more than 100 C-level executives supports the notion that data quality and integration are viewed as obstacles to business analytics success. Eighty eight percent said that departmental silos are considerable obstacles, while seventy six percent considered data quality, integrity and consistency key obstacles to the successful execution of business analytics within their organizations.
Add to this the growing number of customer communication channels (e.g. Twitter, FaceBook, etc.) and the data integration challenge is growing in complexity. Although companies have yet to address traditional challenges, the new challenge is to effectively bring together conventional customer data with new digital information from web analytics and social media interactions. Customer data integration processes need to link to this new user generated content, and management of customer records should include customer communications over these new social channels. These efforts will be complex and challenged to map social data to a traditional customer record.
Organization and culture – the changes required for future success will test even the best change managers. As companies have proven time and time again, it’s usually not technology that undermines our efforts, but politics, organizational boundaries and culture. The stakes are growing ever higher, and success in a world where smarter and faster decisions are needed is tied to a company’s ability to address these challenges. I expect to see new roles and titles emerge in the next eighteen months. For example, I’ve already seen several companies appoint a Vice President of Voice of the Customer.
In light of the critical factors mentioned earlier in this post, the challenges described above, and the growing level of complexity that surrounds every business today – I expect to see a considerable focus on:
- Customer Data Integration and the broader issue of Master Data Management
- Content management and analytics that deliver insight from unstructured data
- Social intelligence
- The growing number of mobile services tied to analytics
- Analytic-enabling architectures and computing power
- The deployment of real-time advanced analytic capabilities
- Capturing the voice of the customer from multiple channels
- Integrating analytics into operations to drive decisions and process
- Enabling action from insight – e.g. a complaint on Twitter routed to a customer support rep
- Outsourced analytics – as analytic resources become increasingly hard to find
- Organizational change
- A stronger focus on change management
- More business user focus and leadership in the areas of business analytics
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