By Grace Liu, Senior VP of IT Strategy and Global Applications, Seagate Technology
Enterprise data is growing fast, yet research published in Seagate Technology’s Rethink Data report has shown that just 32% of enterprise data gets activated and put to use.
When it comes to their data, many business leaders are making the same common mistakes. All too often, what starts as a ‘data lake’ of useful business insights ends up more like a ‘data swamp’, full of dense, inaccessible, and unhelpful data that slows down decision-making, rather than informing and accelerating it.
Here are three tips for businesses to avoid the most common pitfalls and keep your data sources useful.
1. Devise a clear business strategy for your data
Many firms capture and store data, but struggle to maximise its value. Often this is because they lack a core business objective for what they want to achieve with it.
For example, retailers acquire a lot of customer data through purchases. Online firms like ASOS have leveraged this to powerful effect, but in-store electronic point of sale (EPOS) tills will also let you track purchase data. Yet many brick and mortar retailers lack a strategy for what to do with that customer information once they’ve got it.
With the right strategy in place, the retailer could use that data to serve business objectives, such as identifying underperforming products. The company could then use that insight to inform new advertising campaigns or design better products entirely.
This is a broader lesson that isn’t just for the retail industry. We can just as easily apply the principle of identifying business objectives for data to manufacturing. The sector was one of the earliest adopters of automation and robotics, yet while an innovator in this sense, Seagate Technology’s research has shown it is a laggard in data management.
Whatever the industry, the common thread here is that while data is collected, it is often then left alone and underutilised. This is a waste – if you want value from your data, give it a purpose by setting clear business objectives for it and bringing it into your decision-making processes.
2. Capture the right data, then store it right
With so much data out there, organisations need to be able to identify the right information, capture it, and store it securely. Given the overwhelming proliferation of IoT applications and 5G deployments, many businesses struggle to keep up with what they generate. The result is they fail to capture it all.
This is a recipe for wasting potential business value, but it can be avoided. For instance, today many enterprise-class tools can automate the process of capturing and sorting data.
Once a manual task, nowadays Structured Query Language (SQL) lets users focus more on outcomes, with artificial intelligence and machine learning (ML) being introduced to sift through data and look for patterns. This gives rise to near real-time analytics, advanced analytics, and visualisation.
3. Audit your data stores frequently
Capturing and storing data is just half the story. For it to retain value and usefulness to a business, data stores need auditing and refreshing – but this can be a time-consuming process that requires investment.
For many businesses, the answer is a matter of having the right technology. The growth of cloud storage services, along with AI and automation software, has become an almost magical solution for sifting quickly through mountains of information.
The best way to do this is to pick a data set, select a machine learning technique to go through it, and then apply it to others once a favourable result has been achieved. Fraud detection at banks is a good example: AI-based systems are being designed to learn what type of transactions are fraudulent, and then use neural networks to determine them based on the frequency of transactions, transaction size, and type of retailer.
Even data that has aged or is no longer relevant can be transferred to another repository where it can be retained. There’s always the chance data may offer new, yet-undiscovered value. To do so, an enterprise can, again, use data movement services designed to transfer massive amounts of data across private, public, or hybrid-cloud environments.
Keeping your data lakes vibrant
Too many of today’s businesses view data management as an inconvenience, and data itself as a burden rather than a boon: the result is massive data swamps that provide very little value.
Clear business objectives and a robust data strategy that manages capture and auditing will ensure your data stores are healthy, vibrant lakes.
Whether you’re in manufacturing or retail, financial services, or another field entirely, there is no denying that we live in a data-driven world – and everyone can benefit from adopting healthier data management practices.