By Matt Baker, Senior Vice President, Strategy and Planning, Dell Technologies
Drinking hot water and lemon feels very virtuous. It’s a cleansing, detoxifying drink. But not many people use the peel; and yet it’s packed with nutrients. It’s good for your bones, heart, immune and digestive systems. By doing a quick squeeze and tossing out the peel, you’re depriving yourself of its many benefits.
The same applies to data. Data’s potential to unlock new insights and opportunities is motivating organizations to shed their old ways of thinking and adopt a new mindset inclined to spearhead innovation and create engaging customer experiences. This metamorphosis goes to the heart of a digital business and is intrinsic in all they do. It is digital transformation incarnate.
Yet, many businesses are only discovering and squeezing insights from a fraction of their data. Most of their data is being wasted. Even worse, the data that they do manage to collect, store and archive is ‘dark data’- data that’s not actually used to drive a business outcome. Despite broad recognition of the value of data, organizations are drowning in a deluge of data, finding it hard to locate all their data, extract it from different sources and silos, and manage access to the right people. Without the right data, these companies are losing out on revenue opportunities due to missed sales opportunities, lost customers, inefficient supply chains and uninformed strategic decisions.
But it doesn’t have to be that way. In fact, there’s a swell of businesses on the other end of the spectrum that are putting data to work. We’re not just talking about understanding customer buying habits to sell targeted ads. Data’s potential reach and influence extends well beyond that. Data-driven companies are sating the demand for more intelligence by doing the following:
1. Taking Data Real-Time
Data-driven businesses are harnessing real-time insights from unstructured, semi-structured and streaming data to power increasingly sophisticated data-driven use cases at scale.
They’re rethinking their data management strategy in line with the exponential increase in the volume, velocity and variety of data. For instance, they’re shifting their data pipelines as they shift their analytics from post-process analysis to real-time. And they’re augmenting their data by infusing these pipelines with disruptive artificial intelligence/machine learning (AI/ML) capabilities, so their data can now comprehend, act and learn.
2. Empowering Data Scientists
These companies are empowering their data scientists by providing the tools and training they need to spend their time doing higher value, skilled analytical work rather than operational tasks. Some of this training may relate to being able to speak in a business vernacular and to the company’s primary commercial drivers, so they can grab the attention of senior decision-makers.
Crucially data-driven companies recognize that it takes a village to empower data scientists to harvest data that will propel their organization forward. It’s a company-wide effort. If data is now the lifeblood of their organization, everyone has a part to play – whether that means being part of a scrum team or bridging the gap between the data and the business problem in a product manager role.
3. Sharing Data Beyond Their Four Walls
Data-driven businesses are actively enlisting and equipping a far wider base of users – across their organization – to access, share and derive value from data.
Some are putting unfettered innovation above proprietary concerns and making their data open-source, to encourage the free distribution and creation of net new data-driven products and services. These organizations acknowledge that the force multiplying nature of data can’t be unleashed unless access is democratized across the company and their people can self-serve.
4. Focusing on Flow
These businesses are adopting data management platforms that span data-oriented roles from data scientists to IT operations, so each component part can work together to make data easier to discover, share, enrich, and activate.
Recognizing that data is on the move and needs to reach the whole organization, they’re setting-up data pipelines that deliver incredible flexibility without sacrificing impeccable data assurances. By creating these data flows, data scientists are breaking down silos and applying well-rounded, well-traveled data to specific business problems.
5. Being Trustworthy and Transparent
Data governance, data sovereignty, and data compliance are complex, constantly evolving concerns. Data-driven organizations strive to keep abreast of changing guidance and have a keen understanding of their data’s context. Being trustworthy and transparent are their guiding principles. They’re always exploring how to safely monetize their data within these parameters. They know how – or have the software that guides and automates how different data should be used or referenced in reports – to avoid potential data privacy violations. They don’t just prioritize the proper handling of data, they’re also acutely aware of the public’s expectation of privacy. With this enshrined, they go above and beyond government regulations such as GDPR and the California Consumer Privacy Act.
6. Foraging for Anomalies
These companies are constantly looking deeper into the stack to re-examine the performance layer and fundamentally rearchitect how to process and better utilize the data they have, as they seek out anomaly data from unlabeled data sets.
While anomalous data can indicate critical incidents, such as a technical glitch or a change in consumer behavior – they’re rarer to find. However, progressive data companies are now deploying ML to automate anomaly detection. In short, they never settle with what they know.
One Step at a Time
These hallmarks or attributes require a highly effective, data-driven culture supported by modern tooling to foster fluid innovation. Many businesses are excelling in some areas, but few are doing all of these well. In some respects, the acceleration of data and processing gains is creating a scissor effect. Companies with traditional mindsets and manual processes will fall woefully behind – while those with ready access to data scientists and compute resources will reap a rich harvest.
While this might be a modern update of history-old market forces, it runs counter to our belief that technology is a great leveler. In reality, no business should feel excluded or overwhelmed. Small incremental changes can make a significant, positive impact. With the right interventions over time, any business can become a data anywhere, data anytime enterprise.