Many organizations have become adept at collecting data at scale, but too few have mastered the art (and science) of filtering the deluge to identify insights and initiate actions that drive favorable business outcomes and increase competitive differentiation.
Across industries and enterprises, data continues to explode, increasing in volume, variety, and velocity. According to IDC estimates, 64.2 zettabytes of data was created or replicated in 2020, and the amount of digital data created from 2021-2025 will be more than twice the amount of data created since the advent of digital storage. Internet of things (IoT) data is the fastest growing segment, followed by social media and video. Given that one zettabyte is the equivalent of about 250 billion DVDs, it’s understandable that enterprises are struggling to keep up.
Without secure, scalable infrastructure and effective governance processes to sort through all this data—along with a culture that empowers people to put its insights to good use—organizations will face a growing delta between the sprawl of the enterprise data estate and their ability to parlay it into competitive advantage. Many organizations will struggle to use data to support agile decision-making, improve customer experiences, and drive decisive actions—linchpins for innovative and efficient business.
“The gap is between organizations’ desire to put data to use and become more data-driven, versus the actual value they’re getting from all the data they have,” says Ishit Vachhrajani, Director, Enterprise Strategy at AWS. “The challenge is how to tie data to the outcomes or the experiences you’re trying to achieve.”
Part of the challenge lies with existing data infrastructure and organizational processes that have fallen behind, making it difficult to keep up with the growing velocity and variety of data. Yet even those companies that have figured out how to capture and convert data into useful insights may still struggle to fully maximize the value of data and turn it into tangible and profitable business action.
“Many times, folks are crystal clear on the insights, but all the silos and centralized decision-making prevent the insights from being acted upon,” Vachhrajani says.
A modern data strategy
Traditional one-size-fits-all approaches to databases and analytics, driven by the need for efficiency, no longer hold up in today’s diverse data landscape. Extracting more value from this now abundant resource requires a modern data strategy—one that isn’t just a technology initiative, but rather an organizational mandate to manage, access, collect, analyze, and act on data in an intentional way.
To turn things around, Vachhrajani recommends a modern data strategy that encompasses three core capabilities:
- Mindsets: Changing the culture involves executives taking a leadership role in driving visible change, building mechanisms to push decisions on the frontline, and creating an environment of continuous learning.
- Skill sets: Organizations need to expand data proficiency beyond data analysts and scientists, through investment in training and the creation of new functional roles.
- Tool sets: Selecting the right tools means breaking free of one-size-fits all tech that worked in a simpler world but can no longer scale. A scalable, secure cloud environment, along with a comprehensive set of data tools that can work with your ever-growing needs and work with different personas in your organization, is foundational to a modern data infrastructure.
Attention across all three of these capabilities will help leadership teams unify silos across systems, departments, and people, making it easier not just to share data, but also to reduce the distance and friction between where data is collected and decisions are made. This will shorten the time between insights and action to drive better and quicker outcomes.
Using data to its full potential requires more than having just one data store or one data lake; it’s about having a complete, end-to-end solution, with purpose-built databases to store and process the data and a data lake to unite all the data. Analytics and visualization tools are required to extract insights from the data, AI and machine learning will help turn those insights into predictions and add intelligence to applications. Finally, for any of this to work, you need to make sure that you have the right security and governance in place to put data in the hands of people at all levels of the organization.
The ultimate goal of a modern data strategy is innovation, which involves using data to create new and improved ways of achieving business outcomes, whether that’s making better, faster decisions, enhancing customer experience and loyalty, staying ahead of the competition, or preparing for the future. Some organizations already are following that playbook and blazing a path to data-driven business. Here are three:
- BMW Group has worked to stay at the forefront of the automotive industry’s digital transformation by using data and predictive analytics. When it needed to innovate faster to keep up with consumer demands while also providing employees with more data to make decisions, the BMW Group migrated from an on-premises data lake to a Cloud Data Hub. The Cloud Data Hub allowed the company to democratize data usage at scale by integrating analytics and machine learning into the data lake to accelerate the development of new, innovative services.
- Nasdaq modernized its data architecture to accommodate massive data growth, deploying a data lake that enables them to separate compute and storage, allowing each function to scale independently. The transformation allows Nasdaq to flex its compute layer to support the volume of transactions at any given time—for example, when market volatility spiked in early 2020, Nasdaq was able to scale from an average of 30 billion to 70 billion records daily. In addition, its solution reduced market data load times by up to five hours, helping Nasdaq’s economic research team provide more timely insights for better, faster decision-making.
- BP has seen the benefits of using cloud technologies, data analytics, and machine learning (ML) to transform its businesses for more than a decade. BP’s use of data science is growing, as an increasing number of business entities use it to extract value from data and inform decision making. Deploying data science products—such as well sensor data for faster and more accurate production decisions, or algorithms that help drive the performance of wind turbines—has rapidly become business- and time-critical.
To facilitate their journey, these and other data strategy leaders are embracing a variety of best practices. Among them:
- Encourage company leaders to fully embrace culture change, not just issue mandates for others to execute on the vision. Leadership needs to elevate its own understanding of the skills required for data-driven business transformation while making visible changes in how they work and make decisions with data.
- Identify data stewards who can advocate for data and educate others in how to break down silos and close the gap between insight and action.
- Pick the right tool for the right job. Traditional tool sets are unlikely to have the flexibility and scale needed to solve modern data problems.
Harnessing data to reinvent your business, while challenging, is imperative to staying relevant now and in the future. It’s survival of the most informed; those that survive and thrive will put their data to work to make better, faster decisions, improve customer experience and loyalty, and stay ahead of the competition.
Learn more about ways to put your data to work on the most scalable, trusted, and secure cloud.