Why let your data go unused when you can turn it into your cash cow?

It is not an exaggeration to say that the process people commonly refer to as “data analytics” is truly one of the X-factors in ‘going digital’. The digitalisation journey and mastery over the right approach to analytics are critical for the success of digital investments that organisations make.

Data was once important to only a few back-office processes, such as payroll and accounting. Today, thanks to technology, companies can have access to robust and reliable data informing their entire sales to delivery cycle. The only problem is that many organisations may not know exactly what to do with all that information.

In 2006, Thomas Davenport, an academic specialising in analytics, business process innovation and knowledge management wrote the book “Competing on Analytics”, explaining why data analytics is a source of competitive advantage.

“[Analytics competitors] know what products their customers want, but they also know what prices those customers will pay, how many items each will buy in a lifetime, and what triggers will make people buy more.”

Not knowing the best way to read, understand, and apply data can be costly for your business. These costs could take the form of lost revenue opportunities, lower efficiency and productivity, quality issues, and more.

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Forrester reports that between 60 to 73 per cent of all data generated by an average enterprise goes unused; the opportunity to analyse it for better service to the business often gets lost simply because there aren’t effective techniques for capture or analysis governance in place.

And, that’s still happening despite an ever-increasing number of companies talking about big data, using technology to capture more data, and acknowledging the value of this information.

In fact, according to a report by the Aberdeen group in 2015, the benefits of data analytics extend through both the top line and bottom line of an organization. Firms that invest in analytical firepower can boost revenue from net-new buyers, expand the share of customer wallet through cross-sell and up-sell effectiveness, and drive incremental spend through referrals from loyal buyers.

Firms can also make use of analytics to streamline their operating expenses and service costs by identifying root-causes of customer-facing or service delivery issues and take appropriate actions to mitigate these problems.

While the benefit statements seem clear enough, there remains the issue of whether most organisations are setup well enough to handle the vast volumes of data generated by modern customer-facing and operational processes.

Does the organisation have a “Data Lake” or a “Data Swamp”?

A “Data Lake” describes an effective data setup where a vast amount of data from various sources can be ‘ingested’, stored and analysed for business benefit. On the other hand, a “Data Swamp” is one where there is little or no organization system or governance in the capture, storage and use of data.

“Data Swamps” are often virtually unusable by organisations and cause much frustration among rank and file as they struggle to deliver good business performance using flawed, incomplete and often incorrect data about operational processes or customer experiences.

When a business is analysing bad data, investment in data analytics will not provide the expected delta or return on investment. It is, therefore, mission-critical for organisations to invest in good business processes that will help ensure improved data quality, reliability, and generally increase data utilization for analysis.

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The goal, when it comes to the data organisations feed their analytics platforms, is often referred to as the “single source of truth,” otherwise known as the data the business can trust to derive recommendations that can drive the business forward.

Executive sponsorships are critical to ensuring strong data governance and data integrity. Data governance programs should focus their endeavours on activities that will engage senior leadership, such as articulating the dependency of executives’ high-priority initiatives on improved data quality.

Executive sponsorships are also important for securing the funding and resources to support data collection and cleansing activities, in ensuring that front line or support staff all understand the value of these initiatives, and finally, are encouraged to support and enhance the activities that will help these plans succeed.

Last but certainly not least, to ensure transparency and traceability of data analytics investments and activities, the business must clearly articulate its needs and the value uplift desired from such investments. The business should set clear goals and metrics and accountability to support, implement and track business value attributable from data analytics initiatives over time.

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Image Credits: gpointstudio

Are you a key executive or a marketing specialist who is struggling to make your organisations “high-profile” investments in technology and efforts in “data analytics” work? Want to learn more about taming those data demons and getting real ROI on the books? Visit Neel@TheEngage.com for more

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