Enterprises are suffering a scarcity of insight. Think about it. There is an unprecedented amount of data in the world, and it is growing exponentially by the day. In fact, according to IBM, 2.5 billion gigabytes of data are created each day. This data is being generated by everything from smartphones to enterprise databases; scientific, market, and medical research, as well as vehicle telemetry data, and all of that information pouring in from the burgeoning Internet of Things. Everything that can have an IP address gets an IP address, and it is generating data.
There is so much data being generated that it is placing extraordinary pressures on enterprises to properly capture, store, analyze, and then present in a comprehensive, insightful way. In the years ahead, it will be the most successful enterprises that are able to turn all of that data into insight and identify the important changes and trends to best respond to customer and market conditions, such as tightening commodity availability or surges in customer demand. Much of the insight needed is there to be found, but it must be unlocked.
To be certain, to get the most out of this data, enterprises must not only be able to collect it, but they must also be able to harvest it and create meaningful insight. This is a challenge facing not just consumer and IoT businesses – but every business.
Getting at the data that matters
It starts with data collection. If data isn’t collected, it may as well not have ever existed in the first place. Whether that data is coming from far away sensors, a smart-phone or -watch, or applications and databases that reside on-premises, cloud or end-user end points – it needs to be collected and stored. Increasingly data, whether user generated (health monitors etc.) or user created (notes taken from observations) is coming from mobile devices: phones, tablets, smart watches, etc. Where does the data go when it is collected? Too often it goes to some siloed database, or an isolated cloud server, or sits on the device never to be utilized again. What a waste.
That brings up an important point, when it comes to accelerating innovation with data: the data must be accessible. The more centralized the data can be stored, the easier it can be accessed. This design lends itself to cloud architectures. Fortunately, data analytics tools are finally being developed as cloud native. This makes the data accessible to everyone.
Often, when data is centrally stored, it’s still not accessible in a readily or informative way to the bulk of enterprise users. Legacy data warehouses, for instance, don’t provide businesses the ability to capture all of the information they need from where it’s created and then provide that information to the users that need it to more effectively and successfully do their jobs. And when data is available to query, it’s often incomprehensive gibberish delivered in multiple reports that even data scientists would struggle to assemble into something coherent.
Real-world innovation at the speed of information
Big Data is a hot space right now. Too often though we forget the data is only as good as how you use it: What insights can you gain from it? How easily it is to analysis, and how quickly can you get these insights? At Salesforce, we’ve see customers, who were previously struggling with data sprawls and poor account visibility transform dozens of disparate reports into a single dashboard, with that one dashboard providing more functionality than those previous reports combined provided. The result is improved agility and the ability to share and collaborate all of that information with other business teams, executives, and others. This is how data analytics can speed innovation in organizations.
Another example is a beverage manufacturing businesses that had a manually generated scorecard of various key performance indicators and other metrics. They would process scads of data within Excel, format it, convert it into a PDF and share it via email with their management teams, who would then print out these emails and share it at meetings. Not efficient.
This manual process would take about three weeks. The executives at this organization were persistently making decisions based on data that was roughly three weeks old. By implementing data analytics they were able to create a real-time dashboard that is able to access the data where it sits. That means all of the scorecards, KPIs, and other metrics generated can be seen in near real-time. That’s powerful. It’s what we often call a Customer Success Company – one where the customer, and the interactions, come first.
In today’s competitive market, insights like that can’t go hidden. When competitors have access to insight to changing market conditions – they can rapidly improve and out compete and out-innovate those that can’t.