As digital transformation becomes mainstream, digitization is no longer a differentiating advantage. Enterprises must answer to a new set of expectations from customers, employees and business partners, and all while prioritizing compliance with tightening data regulations. To ensure they aren’t hindered by bad data – or the inability to leverage good data – companies must balance both offensive and defensive strategies.
This two-pronged approach is essential to making the most of data as a strategic asset. If enterprises aren’t set up to maximize the value of data, they are not only leaving money on the table, but also putting themselves at risk. With pressure mounting to modernize and optimize, where should organizations start? What is most important when building and executing a data strategy? For enterprises facing the digital transformation frontier, read on to learn when to focus defense, offense and everything in between.
Defense goes beyond regulatory compliance
The surge in data privacy regulations has certainly put pressure on enterprises to develop a defensive data strategy. Data privacy officers are still being inundated with regulatory concerns from every angle, for which most organizations were not prepared. While the GDPR kickstarted the wave of regulatory reaction, it did not incite change at many enterprises that either aren’t multinational or don’t engage with Europe. The California Consumer Protection Act (CCPA) could not be ignored, however. California is the fifth-largest economy in the world, so if organizations want to satisfy consumers – and not get fined – they need to comply. As a result, compliance has been the primary driver in the market for defensive strategies today.
As organizations aspire to move beyond compliance, they first need to get their “house” in order to drive greater value from data. This process starts with building an “as is” data inventory, which collects metadata from the data sources inside the business and externally, so organizations know what data they have today and in the future. Many enterprises skip this step and begin with building a business glossary. However, this step overlooks the critical process of learning what business assets the data is connected to and how to best backfill where information came from, which creates a guessing game. Establishing an inventory is the first foundational step – and while compliance may be a goal, organizations should be driven by an even broader need: establishing trust with data.
The need for trustworthy data is two-fold. First, lack of trust is a bad tagline for any company. Chief Data Officers (CDOs) cannot trust what they don’t understand, and in turn, cannot glean value from what they don’t trust. In essence: you don’t know what you don’t know – which is poor positioning when trying to establish compliance. By implementing an inventory, understanding the entire ecosystem and proactively seeking out dark and siloed data, organizations can confidently tackle compliance and pivot more quickly as regulations and auditors come knocking.
Beyond compliance, trustworthiness is critical for moving corporate culture forward for digital transformation. As CDOs aspire to develop new strategies and derive value from data, they cannot do either while their executive peers use “bad” data to drive initiatives across other lines of business (LOBs). When trust in data is determined on a department-by-department basis, it’s near impossible to find consensus within an organization. If decision-makers don’t trust the information being shared by their peers, they are less likely to consider it seriously and will continue making conclusions based on gut rather than fact – resulting in excess back-and-forth communication and inefficiency. In this case, the impact of a lacking defensive strategy isn’t just measured in fines, hits to brand reputation and breaches, but also in a company culture that cannot progress forward.
Defense and offense go hand in hand
Competing in the information economy means successfully driving data-informed innovation and growth. It’s not just about monetizing data. Rather, success is measured by how fast organizations are able to make smart decisions – from identifying the right market to go into, to making strategic investments. The most competitive enterprises know how to leverage information to grow their top line, not just address cost-savings.
An offensive data strategy is essential for enterprises looking to capitalize on today’s top technological trends, such as artificial intelligence (AI) and machine learning (ML). AI and ML are making incremental improvements to enterprises’ customer experiences, digital experiences and call centers, to name a few – each of which help deliver the personalization and 1:1 engagement customers want. Having the right data equips the direct and indirect salesforce to help those customers on their sales journey. Yet, leveraging AI or ML is infeasible – if not irresponsible – until enterprises establish a trustworthy big data environment. Enterprises need to trust the data they feed into AI and ML programs. If they rely on bad, wrong or non-compliant data, the AI-enabled technology will glean equally bad or wrong insights.
In this way, defensive and offensive data strategies are intimately related. While they both strive to accomplish different objectives, they require the same foundation and are supported by the same types of data management tools. Organizations must start with the same data inventory to understand the good, bad and ugly data, and start taking the appropriate actions. Data intelligence tools can automate capabilities such as data inventory and data lineage so they are up-to-date on an ongoing basis. These tools should also be accompanied by a trust maturity model. To build a mature data environment, organizations must know what data they have. Then they must contextualize that data, govern it and then be able to share it, knowing it is tied to the right business policies.
Moving business forward with trust
Simultaneously driving both a defensive and offensive strategy may seem overwhelming – but ultimately, both are a means to the same (if not similar) end. The goal is to modernize the organization, and IT pros need buy-in from the C-suite to make it happen. As they form their arguments for why investments are worthwhile, they can point to either defensive or offensive justifications. At many enterprises, compliance and risk will be enough to convince the C-suite they need to invest in new technologies, processes and corporate culture. At other organizations, where compliance is already well managed or in control, IT pros may make the case for driving greater value from data as an asset.
Either investment or justification will require trust-enabled change management. Organizations can’t just implement new technology and claim digital transformation victory. They must establish that the data being used to make decisions is trustworthy – and having capabilities such as data lineage makes it easier to explain reasoning in detail. The more that stakeholders in sales, marketing, customer experience and go-to-market business units trust data-informed decisions, the more that corporate culture will become a driver of progress and success.