Thinking digital transformation, think data first!
For digital-first companies, 2020 bore witness to some impressive milestones. COVID-19 accelerated the shift to e-commerce by five years. Amazon’s year-on-year sales soared 37% to over $96 billion. And Snowflake became the largest software IPO in history at $3.4B.
What’s more, nearly 80% of executive decision-makers said COVID-19 increased budgets for digital transformation, and almost all agreed that the pandemic sped up their digital transformation (97%). And yet, despite the forces driving these transformations, only 30% are successful. Why is this?
It all starts with data
Put simply, digital transformations are fruitless without meaningful data. Therefore, before an enterprise can embark on any kind of transformation, stakeholders need to make sure their data is unified. As we see time and time again across the organizations we speak to, the inability to achieve true digital transformation almost always stems from an inability to be agile with their huge and often unwieldy data sets.
This is because both business teams and engineering teams are buckling under the pressure of unsustainable manual processes and/or constant re-engineering to cut the data in the right way.
Barriers to unified data
Garbage in, garbage out: One of the main challenges we see is ‘garbage in garbage out’ at the petabyte scale. As a result, lack of governance is becoming a primary concern for data-driven companies. Not only is governance a legal requirement (teams need to be able to show how they are handling or updating data); but it also increases accuracy (the data is always fresh and correct); and efficiency (easier decision-making based on trustworthy data). Governing one pipeline manually is manageable, but when you have tens or even hundreds of pipelines, there is no way of ensuring proper governance without automating at least some of the process.
Communication silos: Another challenge of unifying data is unifying teams. It’s pointless spending resources on a beautifully engineered data asset if the business team cannot access or use it without supervision. Therefore, it’s vital to remove the silos that so often occur between these teams, so that everyone knows what they want from the data, and how to access it. Read our blog on the emerging data themes as a takeaway from our recent fireside chat with Ameen Kazerouni, the Chief Data and Analytics Officer at Orangetheory Fitness
The build vs buy dilemma: Some teams opt for a ‘build your own’ approach, where they build their entire data asset using open-source development tooling. The problem here is that they face multiple quarters of risky engineering. This is then followed by an endless and growing stream of operations and maintenance expenses, each time a new data source needs to be added to the mix, or the business team requires a different cut of the data.
Others approach the problem from a data replication perspective, whereby they load raw data into a warehouse. However, they find themselves forced to expend an unsustainable effort on data cleansing before it can be trusted and used by business teams. But what if there was another way to harness your growing and increasingly chaotic data sets without spiraling costs or engineering requirements?
A holistic data approach
A more sustainable alternative is to implement a ready-made data integrity engine that connects business and engineering teams. A platform that allows non-technical business teams to use rules (or ‘recipes’) to normalize, aggregate, and govern the growing petabytes of data coming into the business. And all without burdening already-stretched engineering resources.
But it’s not enough to simply implement a turnkey solution. When choosing an automation platform, it’s important to make sure the software is backed up by a customer success team who can get to know the business team’s data requirements from the start and help end-users of the data create the recipes they need according to their own specific KPIs.
Armed with this type of automated data engine, users can access the exact reports and data analytics they need at the click of a button. What’s more, they start to become integral drivers of digital transformation via data transformation.
By using technology that allows for continuous data onboarding – underpinned by essential domain expertise – the post-pandemic era will be a time to capitalize on the forces driving this change.