Gabe has transitioned from being a graduate to working with senior members of the boardroom within a year and has demonstrated his ability to become an excellent data consultant – a journey which typically takes 2-3 years.

With an abundance of soft skills – hosting wide stakeholder audiences, taking ownership of large projects, creating, and reviewing thought-leadership content – Gabe is a mature and integral and key member of the Ascent Data Consulting Team, supporting mid-sized organisations with data strategy, data literacy, and ideation services. With the year, he’s swapped university lecture theatres to demystifying communications to 500-strong teams, and become a master communicator, conducting webinars on prioritising strategic initiatives on the Big Data London webinar platform.

According to Rich Pugh, (Data IQ100) Chief Data Scientist at Ascent, “Gabe has leveraged his strong academic background in data [1st class MMathStat in Mathematics and Statistics from the University of Warwick] to bridge the gap between the art of the possible with data and realistic business ambitions and capably act as a translator for data practitioners and business stakeholders of all seniorities. He mixes this with a common-sense approach, using his natural instincts, high levels of empathy, and attention to detail to ensure an excellent quality of output work and robust personal relationships with Ascent’s customers”.

In 9 months, Gabe has clearly demonstrated his leaderships skills and can confidently lead projects and coordinate all the moving parts. He has an excellent and engaging communication style and has a natural ability to communicate complex technical details to non-technical audiences, tailoring his style appropriately to the situation, from pre-sales conversations to customer workshops to stakeholder engagements.

Congratulations to Gabe Musker, we look forward to following your career journey and certainly celebrating with you at the DataIQ awards night.

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 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.