October 8, 2020

How to build a data strategy that delivers business value for SMBs

Nick Finch
Nick Finch Chief Technology Officer

Business challenge:

Analytics Enablement
Data Maturity
Data Strategy

Data is a vital asset to all businesses, especially as our digital economy is showing no sign of slowing, and neither is the digital landscape in which we operate.

It is therefore critical – particularly in the current climate – that businesses have a solid data strategy in place to ensure they are able to obtain full value and insight from their data.

So, why is data so important for organisations wanting to become more digitally mature? 

Well, digital business transformation is the process of exploiting digital technologies and supporting capabilities to create a robust, new digital business model. In crafting a platform to achieve these outcomes, organisations must rely on and exploit intelligent decision making, for which data and analytics is vital. 

With that in mind, I recently came across an interesting Deloitte study, which was released in early 2019. The study suggested that an organisation can become more digitally mature by developing a broader array of technology-related assets and business capabilities.  

The study identified seven digital pivots whose successful execution can help organisations to realise significant benefits from digital transformation. The seven were: flexible, secure infrastructure; data mastery; digitally savvy, open talent networks; ecosystem engagement; intelligent workflows; unified customer experience; and business model adaptability. 

Of these seven, it recommended that the first digital pivot an organisation should prioritise, and arguably the most important, was achieving ‘data mastery’ — defined as the use of data and analytics to find business-relevant insights that inform better decisions, not just for senior leaders, but for people throughout the enterprise.  

“Data-driven organisations are 23 x more likely to acquire customers, 6 x as likely to retain customers, and 19 x as likely to be profitable as a result.”  

Source: McKinsey, Five facts: How customer analytics boosts corporate performance 

Why is it the most important pivot? Well, by achieving ‘data mastery’ and becoming more data and analytically mature, this can considerably enhance the effectiveness of the other digital pivots. 

And, given that digital transformation is all about organisations using data and technology to continuously evolve all aspects of its business models – what it offers, how it sells and delivers, and how it operates, it shouldn’t be too much of a surprise that ‘data mastery’ is priority number one.  

Customer experience platform, ecosystem platform, Internet of Things (IoT) platform and information system platform create a digital business platform that has data and analytics platforms at its heart.
Source: Leverage Data and Analytics Efficiently to Improve Digital Business Outcomes. Gartner, 2020 

That said, whilst it is widely recognised that digital business thrives on data and the effective analysis of data, you may be surprised to hear that many organisations still don’t have a data strategy, and of those that do, according to a 2019 survey conducted by McKinsey, only 30 percent align their data strategy with their broader corporate strategy.  

This absence of a strategy or misalignment, is usually influenced by the organisation’s perspective of data and analytics as supportive and secondary to their business initiatives, rather than as a strategic business asset.  

This is compounded further in the case of mid-sized organisations, where they are often limited by a lack of specialist resources available for data and analytics, and they are more vulnerable to skills shortages in these areas than their larger enterprise counterparts. 

“Across industries, data and analytics is shifting away from a siloed BI (business intelligence)-focused role to a broader, organisation-wide value driver.  A comprehensive data and analytics strategy that drives business strategy is an essential ingredient for  Data & Analytics leaders to meet the expectation of becoming the digital transformation leader.” 

Gartner – Inside View: Attributes of a Good Data and Analytics Strategy 

Budget constraints within mid-sized organisations can also lead to an emphasis on acquiring technology, rather than focusing on its people and processes. This is a major oversight, as success with data and analytics is not primarily about technology, but about establishing a business-oriented approach to data and analytics.  

This is where establishing a robust data strategy, driven by the business, and enabled through IT, becomes so crucial to the success of an organisation’s digital transformation initiatives. 

With that in mind, in this article I set-out the why, the how and the what, for mid-sized organisations who want to build a data strategy which delivers business value. 

The Why 

To guarantee that you are building a solid data strategy, the most important thing to remember at all times, is why you are building your data strategy in the first place, and who it is going to benefit. 

A strong data and analytics strategy is made up of the data-driven vision, value propositions and stakeholder outcomes. While the vision is more fixed, value propositions and stakeholder outcomes are likely to shift based on feedback from across the business. 

So, in order to get to the strategy, you will first need to understand the why, and have a defined vision for your data and analytics function. A good vision will demonstrate what you will achieve for your stakeholders, both internally and/or externally. 

Once your vision is established, you can move on to the stakeholder outcomes and value propositions that will combine with the vision to establish your strategy.  

It is therefore imperative, that data strategists and leaders must first make sure they have a clear understanding of the business’s short- and long-term objectives and the questions that need to be answered and by whom. By doing so, they will be able to tailor their strategy to meet their business’s individual needs, while providing a clear vision of the desired business objectives and outcomes, for the benefit of business decision makers. 

The What 

The next critical stage of developing a data strategy involves identifying the relevant data sources needed but also understanding the life-cycle of these potential data sources to avoid inaccurate, out of date or non-maintainable data as much as possible which can result inaccurate findings. 

Transparency of costs and governance requirements are fundamental areas to focus on in the initial stages of building a data strategy. To avoid issues along the way, business decision makers should be committed and ‘bought in’ to the potential costs and changes that may be needed such as process changes and assigning of responsibilities e.g. data owners. 

With legislation such as GDPR in place; how data will be processed, stored, and accessed are fundamental factors to think about when building a data strategy. Identifying who owns the data and who in the organisation has legitimate reasons for access to the data plays a fundamental role in the governance requirements. In addition, factors such as retention and archiving must also be considered in order to comply with legislative and organisational requirements. 

A key part of building an effective team is enabling the members of the team to perform in their role and maximise the use of key skill-sets e.g. Too often data scientists execute tasks that don’t fall under their dominant area of expertise. Domain knowledge should be a consideration (such as HR, or Finance experience and knowledge) when building the team so members can bring this knowledge into the project along with their core skill-sets. By pulling together a team with a mixture of skill-sets – from analysts to data scientists and data engineers – placing the right people in the correct places will help to accelerate the overall data strategy. 

The How 

Once business objectives, data sources, governance requirements and skills are identified and established, data strategists can then look to build out a road map and identify how this strategy will look across the business. By focusing on the quick wins in the first instance, data strategists are likely to gain greater support across the business and perhaps increasing budget consideration. Once the road-map has been built out to consider the longer-term objectives, it is important this is published and distributed to ensure the entire business is aware of the data strategy. 

Architecture and design are fundamental elements to be considered by the CTO role when building a data strategy. It’s important not to get bogged down in documentation sets at this stage and instead document through architecture diagrams and process flows. 

Engaging in Proof of Concepts (POC’s) and fast fail design activities can be an effective way to trial multiple approaches with subsets of data, refining through iterations and ultimately informing the target technical design. The maturity of the business in adopting cloud computing technologies should be considered at this point. For a business that hasn’t started migrating to the cloud but has been considering it, conducting POC’s and fast fail design activities in the cloud rather than on premise can be an effective way of proving the extensive benefits and financial model around cloud technologies. 

The approach to solution design is dependent on a multitude of different factors that must be considered. These include the legacy of the business technology in place, where workloads reside (internally or externally – on prem, cloud platform or partner /vendor), pre-existing technology agreements, the budget, regulatory restrictions on the operating environment and the cloud maturity of a business. 

Automating wherever possible through modern development practices such as DevOps is essential and must be considered during the architecture and design phase, done right this can result in highly effective deployment procedures and having the entire architecture reproducible in code and automated pipelines making change control and team collaboration more effective through the ability to build and deploy pre-production environments for demo, development, QA, training, etc. 

The final step in building a solid data strategy is the actual implementation, utilisation and adoption of a data strategy across the business. The standardisation of data collection, transformation and publication is essential. By doing so different business units can have the ability to carry out analysis using well governed, approved data-set for the benefit the of their own departments. Having properly governed data sets and processes in place across the business not only ensures data lineage can be captured and managed but also a consistent approach to handling, managing and analysing data and be implemented.  

This is where cloud data warehouse automation can deliver significant value to a mid-sized organisation. If you’ve not read our Cloud Data Warehousing Playbook for SMB’s then I recommend you do, as we explore this in a lot more detail in the eBook. 

What does ‘success’ look like? 

Metrics are an essential part of your strategy because they will prove the progress and success of your initiatives. When determining metrics for strategic initiatives, many make the mistake of starting with the question “What can I measure?”  

This creates a challenge because it establishes metrics from the current state, not what you hope to achieve with your strategic initiatives. Instead, work backward by asking yourself “What would success look like for this objective?”  

Working back from success will lead you to think about the end state qualitatively, which will then make valuable, quantifiable measures clearer. 

For example, if success for a strategic initiative means data will be more easily accessible for business partners, metrics that would demonstrate that success might include number of new reports created and read by certain teams, or a decrease in time to access reporting data. 

Conclusion 

Often IT teams fall short of achieving business outcomes for their data and analytics initiatives, because they fail to fully understand explicit business needs and desired outcomes. The deployment of technologies including the adoption of cloud platforms must become an enabler for the desired outcome, not the outcome itself.  

The reality is, that a data and analytics initiative will have limited value unless it closely aligns with the needs of those who will use the information to make decisions, if done right it will be aligned with an overall business strategy cascaded through organisational departments. An organisation’s data strategy first requires the vision and then, efficient implementation and continuous review. 

However, with data literacy and analytics maturity being typically low in mid-sized organisations, there must be an emphasis on education and coaching to raise data and analytics literacy levels and to foster a more data-driven business culture across the business. 

If executed correctly, data strategists can empower business leaders with truth and certainty from their data, through a solid, well-rounded data and analytics strategy.  

Business challenge:

Analytics Enablement
Data Maturity
Data Strategy
Written by Nick Finch Chief Technology Officer

Nick Finch is currently CTO at TrueCue leading the engineering effort behind the TrueCue Platform as well as CIO across Concentra. A specialist in building and leading teams focused on delivering scalable, bespoke cloud based solutions and products with over 19 years' experience in technical and leadership roles across data analytics, development, infrastructure, information security and QA.

eBook: Cloud Data Warehousing Playbook for SMBs

If you are an small or medium business looking for a way to give your business the edge, then this playbook is for you. It introduces the concept of modern data warehousing and explains how it serves as the foundation for good analytics. It covers options. It gives tips. It warns of the pitfalls.

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