Results of aKPMG report ‘The Data Imperative’ reveal that 57% of large enterprises do not have a defined data strategy. An actionable data strategy is a critical part of a data and digital transformation that aligns clearly to your business ambitions. It needs to be well balanced and ultimately identify a set of initiatives to invest in that will drive change.

The purpose of a data strategy.

The main driver for developing a data strategy is to create clarity around the role of data in a business’ future success. Many organisations have so much data they don’t know what to do with it. At the same time, thanks to the ongoing hype around AI and analytics, organisations know that there is potential or value in their data assets, but they simply don’t have access to the expertise that could help them identify the best opportunities for their business. This is where a data strategy can help – done well, it creates a clear narrative around an organisation’s opportunity and a roadmap to inform investment as part of a broader transformation.

Creating an effective data strategy that drives action can be a complex task.  Here we look at some of the common questions we get about data strategies:

What do you consider before writing a data strategy.

If there’s one aspect most important to get right with a data strategy, it’s that it must align to (and deliver on) your wider business goals and objectives. Without this, your data strategy will lack relevance – you won’t be able to engage your senior stakeholders – so inevitably it will become expensive shelf ware.

We must also remember that delivering a data strategy is all about change – transforming and reframing your business by putting data at the core of your operations heart. If the organisation is not ready for change, then you may not be able to realise your data-driven potential, however well-described in a strategy.

Why might I need a data strategy?

Firstly it’s important to note that every organisation uses data – you can’t not in this increasingly digital landscape. If you hire a single employee, send a single invoice, or simply send an email, you are using or creating data.

However, the role of data has changed dramatically in the last 20 years – from a by-product of operations to a strategic asset that can be used to create a leaner, smarter, more relevant business.  Because of this, an organisation needs a vision and a plan around its use of data and the role that data will play in their future success.

It comes down to Darwinism: how will you fare if your competitors are able to fundamentally make better decisions than you?  Or if they’re able to cut 25% of their processing costs to deliver a product or service?

What should go into a data strategy?

Fundamentally, your data strategy should create a clear narrative around:

1. Your ambitions: An understanding of why you’re creating this data strategy, and what you hope it will achieve in the context of your business objectives - is it to become more smart or efficient for example? Since many people in the business won’t know what a data strategy is supposed to accomplished, we should describe why we’re creating it and what it will enable the business to do that it cannot do today.

2. Your opportunity: A vision for the way in which data will deliver on your business ambition, including a set of specific activities and the impact these are expected to deliver. For example, consider the following:

• What does a future data-driven version of our business look like?

• What value could data deliver to our business?

• What is the cost of transformation? What is the cost of not transforming (or not transforming now?)

3. Your journey: A clear definition of your current maturity levels, and what will need to happen to support the value you are looking to deliver:

• What is our current starting point in terms of maturity?

• What does a practical and balanced roadmap look like?

• What are the immediate next things we should do?

To describe your journey, the data strategy should present a summary of the current levels of data maturity across an agreed set of pillars which include People & Culture, Technology, Data Management & Governance, Skills & Change. This ensures your data strategy is well balanced (and not just a business case for tech investment, or a suggested data management process).

How should we communicate our Data Strategy?

When your data strategy is created, you will likely find that you will need to create alternative versions to communicate key aspects to a variety of stakeholder groups. For example, you may want to create a version for the board to support an investment case, aversion for broader internal stakeholder engagement and even a public version for external stakeholders.

It is important to remember that a data strategy is a live working document, so establishing a clear plan for ownership, and an approach to communication is vital - forming a dedicated project team from all represented areas of the business is essential, rather than being conducted in siloes.

In summary, an effective data strategy can create alignment in the business about the role data will play in meeting your ambitions, helping everyone understand the reason for your journey and the steps you will take towards becoming a more data-driven organisation.

By Rich Pugh, Chief Data Scientist,Ascent.

#datadrivencompany #datastrategy #digitalstrategy

Have you ever sat in a meeting with your Board or executive team when a really obvious question was asked that nobody truly had an answer for? Do you recall an uncomfortable silence followed by a senior leader providing a half-baked response (whilst two other senior leaders frantically messaged their teams)?

If you do (and you are not in the minority!) - ask yourself this: who should have had the answer? Who owns data in your organisation?

Data should drive ALL business-critical decisions.

The Covid-19 pandemic has disrupted (and is still disrupting) most industries. For some, it has led to a complete standstill for few months, and an urgent need to re-finance and cut costs. For others, it has meant a boost in sales and unprecedented growth.

Regardless of where your business stands in between these two extremes, you can say that it has taught us all how important it is to have accurate, readily available data that informs critical business decisions. Often, this is data which describes productivity per location, per sector, per type of product, per team, per employee, or it simply indicates the actual number of products sold, customers engaged, or employees in the company.

Let’s run with the latter example. If you ask HR how many employees there are, you will get a figure including everyone who has a contract with the company, permanent staff as well as contractors, but also staff who are on unpaid leave, special leave, sabbatical, secondment, etc.

If you ask Finance, you will get a number that reflects staff on the payroll. Therefore, the answers to the same question from HR and Finance will be different – but both can be considered ‘correct’. But which answer should you use to drive your business?

Governance and trust: data roles.

Data and analytics assets exist everywhere across an enterprise and vary in nature – and not all data and information is equal. Gartner suggests establishing a trust-based governance model that:

• supports a distributed ecosystem of data and analytics assets

• acknowledges the different lineage and curation of these assets, and

• assists business leaders in making contextually relevant decisions with greater confidence.

The last point above is key - it all comes down to context. If we consider our earlier example, the scenario might be that the CEO is asking how many employees the company has because they need to decide how many they will furlough. Providing this kind of answer is only possible if the ‘People’ data in this company has a single owner who has a framework in place to steward the relevant data sets and deliver context-specific, relevant answers to organisational questions.

Which brings us to data governance roles. There are various approaches to the delineation of responsibilities around data but one of the simplest (and therefore my favourite), is the distinction between Data Owner, Data Steward and Data Custodian. You can read vast amounts of material on each of these roles from either Gartner or DAMA, but, succinctly, this is what they mean to me:

• A Data Owner is the person accountable for the specific and logical groups of data assets (in our example, all data sets that constitute ‘People’ data), whether generated by the company or 3rd party (e.g., postcode database). The Data Owner can be a member of the executive team or a senior manager with delegated authority and a vested interest in ensuring data is managed appropriately.

• A Data Steward is responsible for maintaining specialist knowledge about their data area, putting into place acceptable use of this data, maintaining necessary records about the data (metadata) and is consulted for operational advice regarding any changes about the acquisition, transformation, storage and consumption of this data (where consumption includes both human and system usage). They implement data strategy enterprise-wide for their data area and are also responsible for performing any transformations required for their data assets.

• A Data Custodian is responsible for a set of data. Data Custodians are essentially data administrators who focus on the ‘how’, rather than the ‘why’ of data management. Data Custodians must communicate and collaborate with the Data Steward regarding any technical activities that impact the data within the Data Steward’s scope.

Here’s how that looks in practice:

Data governance ensures that the right people are assigned the right data responsibilities. It is mostly about strategy, roles, organisation and policies, whilst data stewardship is all about the execution and operationalisation of said policies for the benefit of the whole business, making sure that the data is accurate, in control, and easy to discover and process by the relevant parties.

NB: It is very important we do not mix Data Stewardship in any way with the business function within which the Data Steward happens to sit. The role they perform is company-wide.

In our previous example, the Data Steward for the ‘People’ data may well sit in the HR department, but they are responsible for the single source of truth for a total number of employees, staff demographics, contact details, licences/ qualifications and their validity, etc. Similarly, the Data Steward for the ‘Customer’ data could easily sit in the Commercial department, but their remit is to manage a complete and accurate set of customer data for the whole of the business.

“So what?”, you say. Why should you care about all of this?

It all comes down to a single source of truth. When your Executive asks a question, you want to make sure there is a single party responsible for getting to the answer, using a managed, quality-checked data source or sources. You want to prevent different parties going off on a tangent trying to answer the same question in silos, using locally produced data sets that are not quality checked, resulting in different answers, delivered in different formats with a range of differing assumptions.

What is good is to start asking this question today (not next week, or the week after). The longer you let the business evolve without a clear answer to who the data owners are, the longer you will lack clarity about your business, its performance, and clear lines of accountability.

So see your data for the asset that it is: go ahead, be brave, ask the question. And if you need a hand, the Ascent team is here to help you every step of the way!

Let’s start by playing Strategy Bingo!

Which of these does your organisation have?  Award yourself one point for each:

• A business strategy

• A technical strategy

• A digital strategy

• A marketing strategy

• An HR strategy

• A communications strategy

Well done! Chances are you’ve scored at least 3, if not the full house.  Most businesses today have as a minimum a business strategy, a digital strategy and a technical strategy (note: if you don’t have a business strategy, stop reading now – you have bigger problems).

But over the last 10 years we’ve heard increasing buzz around the latest must-have: a data strategy!  But what is it, and why might we need one?

A data strategy.

Quite simply, a data strategy creates a clear narrative about how a business will use data to achieve its business aims.  Done well, a data strategy is a (short!) practical and pragmatic guide that connects disparate parts of our organisation via the medium of data.  It allows your leadership team to look beyond the ‘what are we doing’ of data to a better understanding of ‘why we’re doing it’ and how it helps to drive real-world outcomes. This should include:

• A simple statement, outlining the vision for your use of data in your organisation

• A mapping of your business goal ‘domains’ - broad themes that connect to deliverable initiatives

• A set of initiatives that will deliver value aligned to identified domains

• Consideration of the impact of the above on pillars of culture, value, delivery, technology, governance.

But isn’t this a digital strategy?

No, although in my experience there can be significant overlap in objectives, energy and approach between the 2 areas. In general, a digital strategy focuses on activities to create new business models that are enabled by the ongoing digitisation of a company. Digital strategies have tended to focus heavily on technical platforms that would enable new functionality in a business – like a platform to support an omni-channel approach in retail.

A data strategy is more focused on the management and application of data and analytics to drive business outcomes. For example, ‘how we’ll use data to better understand our customers so we can have more relevant conversations through our omni-channel platform’.

So, whilst a data strategy has a separate focus to a digital strategy, there can be a symbiotic relationship between the two. They are also both aligned to your technical strategy (which governs the technology approach to create a unified, manageable technical estate) and your business strategy (which describes where the business wants to go and how it will deliver on its purpose).

When is a data strategy not a data strategy?

The word strategy is typically defined as a framework for behaviour – it provides the rules of engagement that govern a set of actions that drive towards a specific outcome, underpinned by a belief system.

However, in business the word strategy can be synonymous with an expensive experiment in inactivity. Too frequently I’ve seen companies pay – er - ‘reassuringly expensive’ consultants to create a beautiful 200-slide piece of shelfware that offers elegant theory but delivers no practical value. To be clear, a good (data) strategy should be:

• Aligned – connected directly to business outcomes

• Actionable – something that can be delivered with clear next steps

• Practical – using language that aligns with internal culture

• Appropriate – approaches are right-sized for your organisation and ambitions

• Balanced – well-balanced across the different ‘pillars’ of transformation

• Concise – as short as it can be to communicate the above, and no longer.

So do I need a data strategy?

Yes! And also, no.

I firmly believe an organisation needs a vision and a plan around its use of data, and a good understanding of how it helps them better compete.  As more companies invest in data and analytics, there is no other option.  How will you fare if your competitors are able to fundamentally make better decisions than you?  Or if they’re able to cut 25% of their costs from a process to deliver a product or service?  There is a Darwinism effect happening right now, so the ‘stick your head in the sand’ approach to data is just no longer viable.

The question is: what is the right vehicle for talking about data?  When a leadership team is all strategied-out, the last thing they may want to hear about is a new and distinct additional paper.  For some, suggesting the need for a data strategy will be met with enthusiasm, but I’ve also seen the idea of a new strategy met with board-level eye-rolling (perhaps due to the issues outlined above).

So here’s the thing: we may or may not need or want a defined, isolated data strategy. What we most definitely DO need is to educate our leadership around 4 key points:

• Data has the capacity to create significant market advantage

• We’re already ‘doing data’ today, so this is about doing it better

• Our competitors are already investing in data (I suggest looking up the latest company report for your competitors and searching for words like ‘data’ and ‘analytics’)

• We need a plan of action around the role of data to drive our business outcomes

Whether you call this plan a data strategy or something else, I don’t think it matters too much.  The important thing is to have the conversation, and to create a clear narrative around the role of data in your future success.

Bringing data to the mid-market is a data conversation by Ascent’s Chief Data Scientist, Rich Pugh (DataIQ 100)and covers all you need to know about data strategy, objective setting and implementation.