Ciaran Doherty, Sales Manager, Client Solutions

In today’s digital economy, it is generally accepted by C-level executives that data informs strategic insights, yet confidence in the quality of the data is often lacking.

True data-driven organisations like Google, Apple, Facebook, Amazon and Ryanair use data analytics as the primary driver of value creation and competitive advantage. Their use of data has effectively transformed the industries they compete in — and the very world we live in.

These behemoths may have been inspired by tech visionaries like Bill Gates, who in a 1999 Sunday Times Interview had the foresight to say:

“Virtually everything in business today is an undifferentiated commodity, except how a company manages its information…How you use information may be the one factor that determines its failure or success — or runaway success.”

The Importance of Data at the C-level

Whether you’re the CEO, CFO, CIO, or Head of Sales, Risk, Compliance, or HR, the right data in your business applications is critical to your success.

Unfortunately, when it comes to data, some C-level executives are shielded from what really goes on under the cover. They don’t fully understand where the data comes from and how it gets transformed, cleansed, and delivered to the CRM, HR, ERP, and reporting systems.

How the data is governed along its path is important — as data moves through an organisation, its quality can be compromised which can have an impact on important business decisions and performance.

Ignoring data quality can come at a high cost to the business — organisations believe poor data quality to be responsible for an average of $15 million per year in losses, according to Gartner. Despite this, nearly 60% of organisations don’t measure the annual financial cost of poor quality data.

So while many C-level executives are aware of the value of the insights unleashed by analytics, business intelligence, and regulatory reporting, many still lack the confidence in the quality of their data and don’t have the required competencies in the company to manage it effectively.

Organisations operating in highly regulated industries like financial services and healthcare were obliged to adopt data management and governance strategies several years ago. Others such as social media, retail and any organisations with access to personal data came under customer and market pressure to also adopt rigorous data management and data governance solutions.  In these industries, we saw the appointment of the first executive-level data roles like Chief Data Officer, Head of Data Security, and Head of Risk and Compliance.

Using regulation as a springboard, smart companies are now leveraging the results of what they were obliged to do, as a competitive advantage. The executives who supported and invested in data are now reaping the rewards.

GDPR was the catalyst that introduced the importance of how data is used across all verticals, however, in most cases this only highlighted the need for regulatory compliance. It did not lead to key data stakeholder appointments or how data is viewed at a business level.

Where do smart executives start?

It’s a common scene: a CEO sits in a board or management meeting where more time is spent trying to interpret the data or discussing whether the data is accurate rather than using insights to make decisions which could benefit the company.

Often the primary action point of such meetings is to validate the accuracy of the data or assess why anomalies exist between similar data sets in different applications or divisions across the organisation.

With such a pressing pain point at the executive level, why is it so difficult to deploy clearly defined data management programs to alleviate common data challenges? There is not an enterprise organisation on the planet that hasn’t identified this need yet the problems persist. To get past these problems and bridge the gap between data and insights, organisations need a top-down approach to data management and a collaborative effort by IT and the business units.

Innovation needs great data management

There are so many new technologies on the market for businesses to harness their data. Many of them include artificial intelligence, machine learning, and robotic process automation. All of them need quality data to deliver results and insights.

Data is instrumental to innovation in all areas of the business from creating efficiencies in supply chain management to delivering effectiveness in marketing. The industry defines the characteristics of great data as:

  • Clean – accurate, de-duplicated, timely, and complete data that matches pre-determined standards
  • Safe – data that’s compliant and secure and only the right people have access to it
  • Connected – data that reflects the whole truth consistently
    Quality data that conforms to these characteristics delivers a solid platform for analytics, as it is:
  • Trusted – gives your decision makers confidence in the facts they are basing their decision on
  • Timely – gets insights in time to take action
  • Accessible – data is in a business-ready state, in a business context, and ready for business use

Consider the immense innovations in customer experience over the past few years. Great data has helped transform personalisation-at-scale to such an extent that consumers expect every interaction with a business to be predictive and automated. They expect the “Netflix effect” for everyday tasks such as placing an order, checking a reservation, or researching a purchase. They also expect businesses to know them, to remember their buying history, to have a record of customer support requests, and so on.

These unified, personalised experiences must be built on a foundation of tightly governed data. But data is often in many places, captured when its significance and value were not recognised, captured in silos throughout the company, captured in different formats or in different applications, all of which have slightly different versions and various levels of duplication without a clear or single source of truth. That’s where the importance of data management becomes clear.

So why isn’t data management and analytics the number one priority in every enterprise today? It’s because data has yet to be seen as a strategic asset and, in many cases, the ownership for data management at a business level remains unclear, falling somewhere between the business and the IT technology function.

Identifying data ownership

While the IT function is responsible for building and maintaining systems, platforms, developing pre-specified applications and algorithms, it is often not close enough to the business strategy and objectives, the risk management considerations, or the threats posed from potential disruptors to leverage an organisation’s data asset effectively.

This presents challenges around the complexity and multiplicity of systems, the sheer scale of data, and the legacy thinking that data belongs to and is the responsibility of the technology team.

An organisation’s leadership team can help identify the right business unit owners to work alongside IT to contextualise the data in a business context. In this way, the business can fully exploit effective data management and analytics.

Governance methodologies provide confidence in the data quality. And from good quality data comes insights, from insights comes action, and from action comes tangible, measurable business benefits.

However, value does not come from simply accumulating more and more data, value is only delivered by using that data to help make better, faster decisions to give you a competitive advantage and protect your organisation from new entrants to your market.

The journey to best-in-class data management will vary from organisation to organisation depending on the scale, complexity of operations, and the markets they serve. A typical journey will take anywhere from six months to a couple of years but is usually iterative and ongoing.

 

Executive sponsorship is key to success

While the best algorithms (Artificial Intelligence (AI) or Machine Learning (ML)) can work wonders, they can’t speak for themselves in the boardroom.

They will only deliver meaningful insights with strong data management, governance, and the help of human imagination, experience, and creativity.

Client Solutions has helped many organisations on their data management and analytics journeys over the last 26 years. Some of our key learnings include:

  1. Throwing money at data management won’t make it a success.
  2. Data initiatives require collaboration between IT teams and business teams.
  3. Executive championship of data initiatives is crucial. The C-suite must sponsor the data strategy from the top down and make the necessary hires to prioritise data as a strategic asset.
  4. Partnerships are key. Talk to industry experts who have solved data problems in the past and know what pitfalls to watch for in data management and governance, learn from experience and insight.

Get in touch today to discuss your data challenges.