November 30, 2022

Bootstrapping your Data Analytics capabilities

Build vs Buy is an age old question that seems very black and white. Implementing data analytics capabilities is different, where a blend of both can allow you to bootstrap your way to meeting your goals without high costs and delayed time to value.
Bootstrapping analytical capabilities

Build vs Buy is a trade-off that many companies often encounter when building new capabilities or during periods of growth. Often the choice is decided by cost and time, but in many cases, companies default to internal capabilities as the deciding factor. Why buy when we have a team of engineers who can build? Or buy because we don’t have in house skills.

This scenario is commonplace in the world of engineering & architecture but becomes much more nuanced when we talk about data analytics. Most organisations will not consider data analytics until it's needed, or as is often seen, have analytical capability hanging off the side of its overall business strategy or architecture as an afterthought of sorts.

Using data to drive an organisation is the hallmark of what we are constantly enabling our customers to achieve. Many organisations are  now realising the benefits and inherent value of understanding customer behaviour, operational performance, cost management and competitor comparisons by making better use of the data they already hold.

This drive to understand data results in a similar Build vs Buy scenario, where most organisations will look to hire a Data Analyst and assign them the mammoth task of trying to make sense of the organisation's mountains of data.

To Build?

As is typical, an Analyst will spend a huge amount of time seeking out, cleaning, and moving data around. This often, in turn, leads to a requirement for Data Engineering skills, and once data is moving there is a need for a Database Administrator to make sure the data is correctly stored, maintained and performance-tuned.

That single Data Analyst very quickly becomes a data team of 3 or 4 people, leading to annual overheads to the tune of £300k-£400k. This cost also yields limited initial results, as there is a lead time to a team producing the required pipelines, analytics, and reporting outputs and thus low return on investment during the first 3-6 months of a Data Analytics initiative.

The above ultimately results in a collection of risks:

Being in the position to build a data analytics function or capability is a great place to be, huge value can be delivered by this function and if managed correctly can transform how a business operates. The ability to build out data analytics and insight as part of an internal function, means requirements and analytical outputs can be tailored to the exact business requirement and the in house business domain knowledge ensures (typically) that the right data can be source to build the right insights.

To Buy?

The alternative to building an analytics capability is to buy a data analytics solution (Integration platform, CDP, SCV, BI tooling). With a vast array of platforms and options available, it's not a simple choice to be made.

Most modern data stacks leverage more than one platform to handle data integration, data storage, data wrangling and data reporting. This means that to create a holistic view of an organisations data, and surface reliable insight there is a significant investment in both time to understand the tooling and how such tools integrate.

As with building a capability, buying tooling also brings risks to the fore:

A blend of Build and Buy

The spirit of what we are trying to do at the Data Refinery is to help organisations quickly gain the business and marketing insight that gets them 85% of the way to being a data-driven organisation. We do this by:

We are not suggesting our platform as a Build vs Buy option, more a platform to build data analytics capability from. We offer enough capability to get an organisation most of the way there, and crucially we provide the hooks to allow an organisation to mature in the analytics space, where it can choose to make use of its preferred BI tool or have the data centralised on the database provider of its choice.

Chart showing cost on Y axis and data driven decisioning on the X axis. Non-Refinery has high cost for high data driven decisioning. With Refinery is low cost, high data driven decisioning.

In terms of the skills required to bootstrap a Data Analytics function or capability, we commonly see stakeholders and the business specialists seeking answers to their questions as key. The Data Refinery can  provide those answers, with more niche or bespoke analytics requirements met by a Data Analyst, that has access to a data platform where the data is ready prepared for analytics.

We say this, because some of our most successful customers have that very combination of skills in play and have gone from zero analytic capability to organisation-wide insights in just a matter of weeks, where value has been realised and Data Analysts recruited because of this, not to try and build from the ground up.

Economy Backdrop

We recognise many organisations are facing the prospect of an uncertain trading period, influenced by talk of recession and customers potentially cutting spending outlay. This makes having fast insight about an organisation’s data, and its customers, paramount. This allows a sharper focus on providing the best experience and communications, and drives that all important return on investment.

It might be that an organisation is hoping to gain  competitive advantage by becoming data driven or looking to save on the costs of maintaining multiple platforms, or even struggling with the shortage of analytical capability on the market. Regardless, finding the most economical but effective choices on how to get through any market shift vital. Enter The Data Refinery, in our view, when it comes to Data Analytics capabilities, it's not Build vs Buy, it’s build from a springboard (with our help).

Interested to know more? Reach out and we can help you get your data analytics capability moving in a matter of days

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Director of Product at The Data Refinery

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