Oakland Group

What are the challenges of building a data platform?

Before we share our best thinking around data platform delivery, it’s worth shining a light on some of the challenges you can expect along the way so you can be well prepared. Introducing a modern data platform to the enterprise is not easy. 

Challenge 1: Excessive Tech-Centric Focus 

It’s easy to think of your Data Platform initiative as a technical project; after all, you’ll soon be designing, launching and modifying an expensive chunk of technology real estate. 

But think back to the failed data management initiatives you’ve observed in past organisations – what did they have in common? Chances are, they got bogged down in the tech at the expense of the business strategy. 

Technical teams often prefer to solve technical problems rather than get involved in the messy business of persuading people with different objectives to collaborate.  

 The big risk in being overly tech-focused is that if your data platform does not meet user needs (such as data availability and data platform usability) they won’t use it. You will achieve minimal adoption and the platform will fail to deliver tangible business outcomes. 

Your data platform aspirations should therefore form the ‘pointy end’ of a data strategy – it’s where the rubber hits the roadmap of your digital transformation. 

 Whenever you feel the narrative swinging too far over to the tech, bring it back with questions such as: 

Without this, you run the risk of low adoption, low involvement from business users, and ultimately low value delivered, if any. 

Challenge 2: Departmental Data Silos 

In an attempt to solve a tactical or near-term challenge, departments or cross-business functions can often be swayed by the allure of a solution vendor’s shiny offering. The department then commissions a localised solution that seemingly fits their needs but doesn’t take stock of the wider data strategy or needs of the business. 

Other teams then struggle to extract this new data, particularly if the department has used off-the-shelf solutions. 

The result is an ever-increasing technical burden that becomes difficult to unravel and migrate in the future. 

Challenge 3: Poor Quality Data

One of the most common issues we have to deal with is poor quality data. Your data platform will only deliver your business goals if the data ingested is of a high enough quality, otherwise your platform could sink without a trace.

Challenge 4: Lack of Data Governance 

“Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.” (Gartner) 

The demand for Data Governance originally emerged from the shift toward more robust regulatory controls in the banking and insurance sectors.  

Today, Data Governance is pervasive across all industries you can even now buy data governance platforms! Yet, many data platform initiatives stutter or fail when Data Governance is immature or lacks key components suited to Data Platform strategy and management. 

When Data Governance is missing from your Data Platform initiative, you create a situation where:  

To find out more about Data Governance, check out our guide: How to launch a Data Governance initiative by Stealth 

Challenge 5: Failing to consider the complexity and cost implications of the legacy data landscape 

Your data platform is not an island; it needs careful integration with existing systems and processes. 

At Oakland, we’ve been around a long time (three decades and counting), and one of the recurring trends we’ve seen is the case of the ‘over-optimistic’ target vendor. 

Despite the glossy marketing blurb, no data platform is a true plug and play solution, the amount of times we have heard vendors describe how their solution seamlessly integrates with existing tech but then can’t explain in any detail how.  

Many aspects of vendor-lock are inevitable and using a vendors portfolio of cloud-native services increases lock-in although access to integrated services and increased discounts can be a plus. To prevent lock-in (eg open source software solutions) make decisions on a case by case basis and vet the total cost of ownership (TCO of solutions intended. 

Creating a new data platform into any enterprise requires a careful analysis of what approaches have gone before and now require direct integration with your new data architecture (Overlooking the need for a robust Data Architecture is something we’ll cover in another blog). 

We’ve parachuted into several data platform recoveries where the complexity of integration was overlooked and soon became the mother of all obstacles to going live. 

In short, don’t overlook the essential brownfield discovery tasks that some vendors like to gloss over in their haste to get you over the finishing line. 

Challenge 6: Not prioritising requirements 

“So, what are we building again?” can become a common challenge as you get deeper into data platform delivery. 

 The problem is the modern data platform strategy can support a plethora of use cases including: 

The list goes on and on… 

It’s easy for your data platform to lack clarity and prioritisation around its core function, especially as different groups begin to see your platform as a data ‘dumping ground’. The practice of “let’s keep it in case we need it” can lead to a bloated data platform, further complicating the task of getting insights out of your data. 

To prevent a toxic swamp of data, we prefer to phase the delivery of a data platform with a regular cycle of ‘Lighthouse Projects’ that solve burning issues within the business but still align to an overarching data strategy and architecture with a clear transition to an enterprise solution. 

Start small, think big, and act fast. 

You get to demonstrate the benefits of each release, garnering support as you deliver each successful project, helping to justify the investment of further initiatives. 

Challenge 7: Creating the case for change 

Creating a compelling business case for a modern data platform can be challenging for many organisations, particularly when faced with a legacy of delivery struggles. 

There are multiple benefits and uses cases for deploying the next generation of data platforms that will appeal to a range of leadership sponsors. 

We’ve found that the key is to deliver smaller, faster pilot projects that deliver rapid and sustained gains without over-investment and risk, whilst building data capabilities at the same time. 

What does the business really want to know? Where do they need insights?  

By focusing on delivering the right data at the right time to support specific business outcomes, you’ll quickly gain support for the future of your data platform. 

Over the years, we’ve also assembled a portfolio of transformation stories that highlight the impact of data platform introduction, so feel free to reach out and learn more.