How data teams move from offense to defense in 2023

It’s well acknowledged that data, when used correctly, has the potential to be a strategic growth asset driving innovation – and with the recent developments in large language models (LLM) for AI, data is really having its day in the sun. To win the game, you need a modern, future-proof business plan. And we’ll let you in on a secret: this means nailing your data strategy.

All of this renewed attention on data and AI, however, brings greater potential risks for those companies that have less advanced data strategies. For organizations that have neglected fundamental criteria for proper data use – like data quality, data governance, and data security – this advent of AI models available to the masses poses more risk than potential gain. As these trends continue to evolve, building your data strategy around the principles of openness and governance assures trust in the data. This means that you can leverage all the emerging trends around automation, large language models, and more. But it all depends upon a solid, trusted data foundation.

If you’ve built that foundation, then you’ve solved other potential problems like: If I decide to share data for business reasons, do I know if I can trust the data to be accurate? Can I also trust the mechanism by which I share that data without exposing myself inappropriately to risk?  When you’ve built a strong governance and compliance program, you can confidently answer “yes” to such questions and even make decisions that may introduce some calculated risk.

This involves a mindset shift, and, of course, a comprehensive data strategy. If you don’t already have a mature, sophisticated strategy, you may have concerns. Chief among them may be: the economy is tight; can we manage the switch? Can we get our strategy into the shape we need before it’s too late? Can we afford the necessary technology to underpin our new strategy? The answer to each of these questions is yes. Never miss out on creating value in the name of cost neutrality or cost take out. In the long run, the goal is to use data proactively to generate growth.

“The role of data is evolving in the next five to ten years from post-activity to being the activity. The database world where I grew up was about data at rest. We did things and we recorded what happened and we stored the results. Then we went back and analyzed that data at rest. We have now moved upstream to where we are analyzing and acting upon the events as they happen,” said Adrian.

Using data to drive decisions is clearly the ultimate goal of a strategy-driven approach to data. Most organizations know that they can leverage data insights to identify new opportunities, optimize processes, and improve customer experiences. But that’s often easier said than done, and new options for automation, AI, and personalized interactions in real time are changing the game. Now to remain competitive, organizations must manage exponentially more data, in near real-time, to make better, smarter, faster choices about almost every aspect of their business. For one manufacturer, whose predictive maintenance insight convinced the FAA to extend the time between services, allowing more time in the air, and so shifted from producing helicopters to providing the ability to transport people or product through the air.

The former Gartner VP Analyst suggests organizations should move from managing technology to thinking about what this technology can do for us. This is the posture change that organizations need to take. Most boards of directors are ready to take risks. Most boards understand that we’re in the post-digital era, meaning that we’re not still in the transition, we are living the reality now.

“At the level of strategy where a company shifts to the offensive and says our priorities are going to be about new, developmental, transformative initiatives that are going to change the way this company does business for the following reasons: that’s a board-level decision. The CEO may be responsible for driving it, but even he doesn’t decide that’s what’s going to happen unless the board is on board. So, seeing what boards are saying is really useful,” said Adrian.

“Establishing metrics and milestones is how you not only sustain executive interest, and therefore continuing support, but also how you can internally prove to yourself that what you’re doing is working. The idea here is that you shouldn’t just pick your smartest guys and say, ‘Okay you go figure out this new thing.’ Executive management has to be more involved; it has to define the objectives, decide how we’re going to measure them, and then judge whether we’re getting where we need to be going or not,” he said.

One industry that has effectively made this shift from service to strategy is the financial services industry. Financial services firms have long been data-driven, but in recent years, they have shifted their focus toward using data to drive growth and innovation. This has resulted in the development of new products and services, improved customer experiences, and increased profitability.

Some banks like OCBC, a multinational bank based in Singapore, are using AI to personalize offers and speed up interactions on their website by using chatbots. Other banks are using data analytics to develop personalized financial products and services for customers and machine learning models to detect fraud and prevent money laundering. As well, data visualization software provides real-time insights into customer behavior and preferences.

Data and IT teams can also become a value creation center for organizations when used proactively and effectively. Your IT team may currently be playing defense, functioning primarily as a service center, monitoring performance, and addressing problems as they arise. Sound familiar? Well, with the right mindset shift, that could change. IT can help to identify opportunities for innovation, exploring new technologies and ideas to stay competitive and drive growth. This can include exploring emerging technologies such as AI, blockchain, and the Internet of Things (IoT), or identifying where process automation could apply to supply chain management, customer service, finance, and so on. And these are just a few of the examples where IT can drive growth – leverage your team’s skills beyond service requests!

“The folks who used to track the weather learned that they could sell that information to people who have crops or have logistics planning. They were willing to pay for some serious analytical looks at weather patterns,” said Adrian.

Automation will radically transform operational costs, which will enable more corporate resources to focus on growth-oriented versus operational activities. If your developers and data scientists are not spending all their time cleaning pipelines and redoing data segmentation and building indexes, they could be thinking about how to implement processes or new products that will generate revenue, save time or money, or expand business opportunities in new markets.

In essence, if you can have a mechanic working on your car or you can have a race strategist show you how to operate the vehicle to give you an advantage over competitors, helping you decide where to speed up, and which curve is best to make a pass – the choice is obvious.

“The biggest challenges companies face [is] the effective use of legacy data and integrating it with the new. There are lots of people talking about the cool new stuff, but many of them don’t have an answer when you ask them about how we can integrate this with our legacy stuff. One of Cloudera’s advantages is that they’ve always known how to do that… they always had that linkage; the catalog and the Hive meta stores were built in order to ensure that we could connect those things,” said Adrian.

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