For many years, data and analytics were the preserve of IT departments within organisations. Large teams were built to deliver analytics ‘to’ the business.
On-premise data warehouses were built with a reporting layer on top with both elements typically requiring installation, configuration and development activities, meaning requests often took months. Often, when analytics requests were eventually returned, they did not provide what people wanted from them, mostly because of changing requirements or misunderstanding of requirements.
To make matters worse, changes to underlying transactional systems typically broke the reporting facility and could take weeks or months to fix, leaving business functions frustrated with the concept of data and analytics.
However, shiny (and often excellent) new ‘self-serve’ products began to emerge as the field of data and analytics progressed. Proactive analysts within functional teams began to use these products to produce their own analytics and upskill in certain areas without necessarily keeping a holistic view of the top-level strategy of the organisation they were working within.
As such, these ungoverned activities would result with organisations having multiple platforms, siloed data stores, differing KPIs, definitions and calculations. In turn, this meant different teams were taking different approaches to analytics and emerging with conflicting insights that decreased trust in the overall analytics process throughout the organisation. Additionally, a lack of governance often introduced risk around privacy and security.
This misalignment often led to stale & under-utilised data stores or data warehouses managed by the IT department (and often more of a dumping ground for copies of databases), and a myriad of uncoordinated analytics run within functions – a bit of a “Wild West”.
Clearly this doesn’t work for IT, nor “the business” and what we are seeing as an emergent theme is one of the generation of data and analytics as a core business function.
This involves a shift from IT employees as technical owners to a team of people whose job it is to truly bridge the gap between what the business is demanding and the technical know-how with regards to how to deliver it. There are a number of models that can be used to help realign IT & business objectives, one of which is a centralised data and analytics team with additional analysts placed throughout business units. This is an increasingly popular model that addresses the fragmentation challenges mentioned above in this blog.
This centralised model can provide traditional analytics capabilities while taking advantage of modern data warehouse automation products to develop and maintain your data warehouse. It provides a secure, stable, single source for data and analytics, trusted throughout the organisation. It can also be used to set and monitor company-wide policies around:
- Architecture & Systems
- Culture & Skills
From an organisational perspective it also provides, with its top-down approach, a more direct connection to senior management and closer relationship to business strategy.
The alternative to a centralised model is, of course, a formalised decentralised structure. This means the data and analytics function, whilst having a direct reporting line into data and analytics leadership, has analysts dispersed across the business, within different functions.
Some clear benefits of this model are around development of subject matter expertise, relationship building and allowing the function to control prioritisation of work for that resource. This model does, of course, retain many of the challenges around losing consistency and best practice, and removes the ability to flex workload across functions as demand rises and falls within departments.
There are pros and cons to each of these two operating models, but it doesn’t have to be a binary choice for businesses looking to become data driven. In fact, we believe that you can enjoy the best of both worlds by combining these two models, in what can be termed a ‘hybrid-federated’ approach.
In this model, you have a central data & analytics team but also have members of this team distributed in the business functions. These distributed individuals would typically report to the centralised D&A team lead/manager but would need to be aligned to the management of the business function in question, developing the contextual knowledge of that particular part of the business.
Whichever approach your organisation opts for, the crucial thing is to bring your data and analytics facility in line with the rest of the business. Your data and analytics team must:
- Work with management throughout the business to ensure alignment with data and analytics strategy across the business
- Establish an appropriate governance model – Managing the data demand pipeline, reporting lifecycle management and training
- Consolidate the available tech landscape to optimise on performace and cost whilst ensuring sustainability. This means designing your data and analytics architecture to cope with future growth (both in terms of size and complexity)
Data and analytics are already such an important part of business, and its role is only going to grow as the knowledge and tools that facilitate it improve. In order to not lose out on the benefits organisations must become data literate, and part of that transformation involves the establishment of a dedicated data and analytics team.
Whether you choose a centralised, decentralised or hybrid-federated structure, it’s vital to align the function with business goals if your data and analytics approach is to become successful.
This article is part of our Data-Driven SMB series. For more information, advice and resources on how to accelerate your organisation’s data and analytics maturity, click here or contact us today.
Bill is a Business Development Director at TrueCue and is responsible for helping existing and future clients gain value from their data and analytics investments.
With over 20 years of experience in Management Consulting (AT Kearney), Industry (BOC Gases; Eli Lilly) and Private Equity (LDC; Easynet) Bill has worked in, and successfully led, teams across several operational functions including marketing, sales, delivery, project management, client success, and support.