Date: 13 May 2022
Strategy should always come first. So why do so many data projects begin without a clear vision of the big picture? Why do so many organisations invest in technology solutions before they have a clear understanding of what they want to do with their data?
A data project shouldn’t be any different to any other organisational investment. And the fact that any technology investment should be rooted in your organisational strategy isn’t a controversial idea. After all, all business investment ought to be driven by a clear strategic vision – so that expenditure is aligned with organisational business plan in a way that will deliver on the stated organisational goals.
So, why then, do so many organisations fail to develop a coherent data strategy before spending money on their data projects?
Perhaps it is fear or a lack of understanding: data science is often presented as some sort of “dark art” rather than the methodological science it is. Or perhaps it is a desire to “get started” quickly – commentators often advocate an agile approach to data projects without recognising that there must first be a strategic vision behind agile methodology.
Whatever the truth, the strategic vision must come first.
Why do you need to define a data strategy first?
Let’s consider the risks to your organisation if strategy doesn’t precede investment.
Information overload is a real problem for many organisations. The sheer weight of data generated by today’s digital world presents significant challenges. First and foremost, there is the cost of data storage which can easily get out of hand if not properly managed. However, storage costs pale into significance when we consider the cost of compliance. There are the practical responsibilities of information management, such as responding to data subject access requests. Added to this, organisations must manage the cyber security risks. Failing to fulfil these requirements or falling foul of compliance rules can be very costly indeed. The best way to mitigate all of these obligations and risks is to reduce the data your organisation holds to an absolute minimum. This requires a clear understanding of what data you need to hold and why – in other words, a coherent data strategy.
Given that breaking down siloes of information is frequently cited as a key organisational objective, it’s really important that your data efforts are well targeted. We’ve seen numerous data projects which have ended up creating just another silo of information – exacerbating, rather than solving, the problem.
Without positioning your efforts within your overall goals and acting with clear strategic intent, you risk making your data problems worse – rather than better.
There can sometimes be a tendency to view data architecture as a tactical exercise, rather than something you should consider upfront. However, there are always some important architecture decisions which will need to be taken early on. For this reason, it’s worth involving data architects in your visioning and strategic planning processes.
This way, you can develop architecture principles at an enterprise level. These can then be filtered down throughout the organisation, across all data projects. As long as these principles are sound – and adhered to – you can avoid many practical problems. When you allow individual projects to go off and do their own thing, that’s when you run into problems. Avoid this by taking a strategic approach early on.
How can you properly prioritise projects when you aren’t clear on the overall objective? What criteria are you measuring those individual projects against? While you might have a real desire to get started and an eagerness to deliver practical results quickly, you’re just wasting time if you get started on the wrong thing.
This is especially true in organisations in which an agile approach is advocated. While we are big fans of agile methodology, we also recognise that it can sometimes be used to justify a scattergun or ill-planned approach to projects. While early proof-of-concepts that demonstrate measurable value and return on investment are important to ensure success, these proof-of-concept projects should be carefully chosen within the broader context of your strategic vision.
If you aren’t strategic in your choice of and approach to projects, then you cannot be certain that your efforts are truly targeted on what the organisation wants to achieve.
If you aren’t strategic in your choice of and approach to projects, then you cannot be certain that your efforts are truly targeted on what the organisation wants to achieve.
Given the number of technology vendors pitching in this space, it is unsurprising that business leaders are often tempted to lead with the technology. However, this would be a mistake.
Technology isn’t the difficult part of a data science project – there are usually several solutions that will meet any need. However, if you invest first and ask questions later, a poorly thought-through technology decision can soon become a burden.
In a strategic approach, you should consider which questions you want answers to first. You have to do this before looking around at how you might answer the questions. Or how to present the answers to the people who need them. Otherwise, you can easily end up with some fantastic new software with sleek features and interfaces but with yet-unknown functional incompatibilities that is doomed to become, over time, yet another siloed system.
The importance of a clear, strategic vision
Strategy must come first:
Tactical decisions about:
will all follow from your early strategic work.
If you haven’t yet done the early work to define your organisational strategy, it’s not too late – and our team is on hand to assist.
If you’d like help with strategic thinking or wish to discuss any of the ideas raised in this article, please get in touch.
You can reach us on: 0208 004 3015
Or email us at: connect@trellisi.com
What are the risks to your organisation if strategy doesn’t precede investment
Collecting and keeping data beyond purposeful need
Creating more siloes of information, rather than fewer
Legacy or unscalable architecture that makes future analysis more difficult
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Shaham is a seasoned Senior Pricing Consultant with over two decades of experience in strategic pricing, specialising in SaaS and cross-industry applications. His expertise lies in developing and executing value-based pricing strategies that significantly enhance revenue and profitability for both B2B and B2C markets
With a particular focus on bundle and package pricing, Shaham combines creative approaches with rigorous analytical methods. His collaborative management style has yielded substantial results across diverse sectors, including software, technology, information and telecom services and financial services
Shaham’s client portfolio includes notable organisations such as Board, Zellis, BT, Ricoh, RBS, HSBC, Honda, Visa and The Guardian. He has successfully led critical pricing transformations, implemented advanced pricing frameworks, and optimised operational efficiency through technology integration
His expertise in salvaging underperforming pricing programmes, minimising margin leakage and enhancing market share has consistently delivered value to his clients in dynamic global markets
MBA in Business Management and a BSc in Industrial Systems & Business Management.
Tunde brings a breadth of experience and expertise in pricing and analytics across B2B, SaaS and B2C verticals, developing pricing strategies, pricing models and implementing enterprise BI and analytics solutions.
He has successfully led and delivered projects and advisory support to clients in the UK, Europe and USA, working with executive management, business unit leaders and cross-functional teams from blue-chip corporates, SMEs and private equity portfolio companies across a range of sectors, including retail, technology and fintech, travel, transport/logistics, utilities and telco.
After over a decade of working as an independent consultant for large and mid-market companies, Tunde founded Trellisi in 2020. As a pricing and strategic analytics leader, Tunde helps businesses to harness the transformative power of data intelligence, pricing and advanced analytics to drive top-line outperformance and EBITDA growth.
He holds a Master of Science (MSc) in Decision Sciences and Management, and a Bachelor of Science (BSc) in Computer Engineering.
Ayan, a highly experienced Customer Success and Marketing Manager, has a background in both SaaS and CRM. With a proven track record of success for enterprise-level clients, she applies strategic thinking and creative problem-solving to help drive customer success, marketing initiatives and project management.
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Danny is an expert in digital transformation and data strategy. With years of involvement in the tech industry, his most recent role was heading up the UK Ministry of Defence Digital unit as Head of Data, AI & Automation, where he established a Centre of Excellence for data governance, management, and AI strategy.
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As an independent consultant, Danny has been providing data-driven solutions to help organisations achieve their digital transformation objectives. With his expertise and leadership, Danny has helped businesses seeking to move forward in a digital age reach their goals in a timely and cost-effective manner.
Zoltan is an experienced Machine Learning and Data Science specialist with extensive experience in designing, deploying and managing large-scale ML applications and high-throughput data pipelines. He holds a PhD in Computer Science from the Computer and Automation Institute of the Hungarian Academy of Sciences, with a focus on solving voice recognition problems using AI and ML algorithms.
He has undertaken a range of data science projects across various industries, such as fraud detection, credit scoring and rate making in financial services; rotational churn modelling and prediction in telecoms; and campaign optimisation and customer lifetime value analytics. He’s a self-confessed data geek and committed to helping organisations realise the potential of harnessing the power of AI/ML and data science solutions to elevate business performance.
Simon Metcalfe is a highly accomplished analytics consultant and trainer with extensive experience in translating complex business problems into data-driven solutions. His impressive portfolio includes successful analytics projects such as churn modelling, customer retention, segmentation and next-best product recommendation for clients in the retail, FMCG, telecommunications, e-commerce and not-for-profit industries.
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Larry is a trusted advisor in data strategy and innovation advisory, boasting a successful portfolio of leading data focused GTM initiatives across a broad range of industries. His expertise includes strategic insight, data innovation and go-to-market executions for government, education, technology, health, financial services, digital and energy.
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Chuks is an experienced and AWS-certified data engineer and BI architect, boasting over 10 years of expertise in data architecture design, data warehousing and business intelligence solutions.
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