Saturday, November 25, 2017


‘CEOs desperate to quantify the value of data and analytics,’ warns Information Builders chief



In a world ever more built on data, 8 in 10 CEOs say they are unsatisfied with the value generated from investments in data and analytics.

Data and analytics is this week driving several thought leaders to the British capital in search for answers around the huge lack of satisfaction surrounding data.

Conferencing at Gartner Data & Analytics London, leaders will discuss the latest data which suggests that only 19% of CEOs report being satisfied with the value generated from their investments in data and analytics.

Data Economy (DE) sat down with Michael Corcoran (MC), chief marketing officer at business intelligence and data analytics company Information Builders, who is also attending the event to discuss more.

 

DE: Why are the vast majority of CEOs unsatisfied with the value generated from their investments in data and analytics? 

Michael Corcoran, CMO at Information Builders

MC: The good news is that most executives and organization leaders finally recognize that data is as valuable an asset as financial capital and human capital. Similarly to those other assets, data needs to be put to work in order to quantify its value.

Businesses have been collecting data for decades and we have finally reached the point where most people recognize that analysing data is more valuable than merely storing transactions.

CEOs are desperately seeking to quantify the value of data and analytics, and ultimately monetize it. Unfortunately, most organizations have made data and analytics available to less than 25% of their employees, and have yet to address the needs of business partners and customers.

The unserved 75% of employees, and the partners and customers outside the firewall, represent the best opportunities to operationalize and monetize their data and analytic investments.

 

DE: With only 19% of CEOs satisfied, the problem seems serious: what sort of board actions are needed to change this? 

MC: There are some very positive developments occurring, such as the introduction of the Chief Data Officer and Chief Analytics Officer roles. These are top-level, sometimes Board-level, positions focused on how the organization manages, governs, and derives value from these data assets.

Executive teams need to adopt an aggressive strategy to make data and analytics pervasive throughout the extended enterprise.

The overwhelming majority of analytic activity is focused on back office analytics to generate management dashboards. Providing management with dashboards for insight is valuable, but on its own does little to alter employee performance or the bottom line directly.

Conversely, when analytics are operationalized to account executives, call centre operators, field technicians, police officers, nurses, etc., we see immediate dramatic improvements in individual performance and the associated impact on finance.

When extended out to suppliers, distributors, service partners and customers, the impact on direct revenue is even more evident.

Analytics is often employed to analyze customer data, with the goal of “Know Your Customer”. If you really knew your customer, you would realize that they want direct access to self-service information and analytics as well.

 

DE: What will the role of the CIO and CDO be in that transformation and how will this match the CFOs and CISOs expectations of low costs and high security? 

MC: We have witnessed a philosophical shift in the relationship between IT and the business as related to data and analytics. Traditional business intelligence tools were considered IT centric as they required extensive data modelling and longer time to value.

Many of the newer visual “self-service” analytic tools are considered more business centric as they emphasize ease of use without IT involvement and often without metadata.

The challenge is that these tools promote a silo approach to analytics, lacking centralized data and collaboration. This combined with the lack of metadata is generating a new level of information discrepancy within the enterprise which is difficult to audit, similar to spreadsheet-centric environments.

The pendulum seems to be finally shifting to a place where there is a greater balance and collaboration between IT and the business.

The CIO and CDO/CAO roles need to work together to ensure that data is easily accessible, accurate and consistently governed, and that analytics are faster and easier to generate and consume.

 

DE: What are the common pitfalls of big data and business intelligence programmes and how do you avoid these? 

MC: Big Data and business intelligence/analytics suffer the same fate. Organizations tend to isolate these technologies and limit their exposure.

Most organizations are trying to figure out what to do with Big Data, so they create Data Lakes where specialized data scientists and business analysts can access them with analytic tools with the hope of finding some “golden nuggets” of insight. Big Data assets need to be operationalized and monetized.

There is an important transition from “hype” to “reality” that often occurs in technology, and Big Data has reached that stage.

The issue that will legitimize Big Data is not technology, but the business potential of the Internet of Things (IoT). Big Data was first focused on clickstream and social media analytics, which is of value to e-business, consumer product, and entertainment industries.

The explosive growth is now coming from machine generated, location and home automation data.  We are seeing exciting implementations in the automotive, manufacturing, transportation, logistics, local government and utilities industries.

More importantly, Big Data is also now being exposed to operational employees, partners, customers and citizens through online reporting and self-service analytic apps.

 

DE: What are the stages that organisations must follow in order to be able to rely on and generate insights and revenue from data? 

MC: Data and analytics are a critical component of your Digital Transformation. There are 4 critical stages of the strategy to ensure the journey is successful.

Stage 1 is to Harmonize all data, including data warehouses, Big data, operational data, social media and cloud data.  In recent surveys, almost 60% of organizations stated they do not trust their data.

Data needs to be integrated, federated, cleansed for quality, and mastered for consistency across all systems. A strong Data Governance strategy should be employed, encouraging strong collaboration between IT staff, data stewards, and business users.

Stage 2 is to Visualize the data in order to generate new insights about the business.  Today’s new analytic tools should allow business analysts to easily generate data visualizations, reports, and dashboards to solve problems and identify business trends.

Analytical tools should scale and incorporate advanced analytical functions such as predictive analytics, search, text analytics, machine learning and AI.”

Stage 3 is to Operationalize those insights by deploying easy to use analytic applications, portals and interactive documents to non-technical employees and business partners.

The focus here is to provide operational decision support for ongoing questions, as well as comparative performance metrics to motivate and drive performance. These deployments improve cost control, reduce waste and deter fraud.

Stage 4 is to Monetize data and analytics by deploying portals, embedded business intelligence, and interactive e-statements directly to customers.

These analytic deployments will generate increased revenue and customer loyalty. Some organizations are discovering they have a new opportunity to create direct revenue generating “data products” as well, providing new value for their customers and a new future for their traditional business.