The Data-Driven SMB Part III: a focus on strategy and process

This is the third article in a five-part series, providing insight, advice, and guidance to small and medium-sized businesses on how to become more data-driven in an increasingly digitalised economy. Complete our live benchmarking tool to compare your approach to data and analytics with small and medium businesses from across the UK

Modern organisations have always relied on data to support their operations. However, with Covid-19 bringing a level of global disruption unseen in most of our lifetimes, the importance of having complete visibility and truth around business data has never been more critical. 

Alongside this, with the growing proliferation of powerful data collection and analysis tools, businesses of all sizes can now access valuable insights once reserved for the biggest corporations. Small and medium-sized businesses (SMBs) must therefore use these advancements to transform the way they operate.

C-suites are indeed investing more in the process of reorientating to a data-driven approach, borne out by research from Gartner, which found that 77 per cent of CEOs are planning to increase their investments into digital capabilities over the coming year.

That being said, while digital transformation efforts have accelerated, business leaders must recognise that investment in the right technologies can only get them so far. Failing to instil an organisation-wide culture of data and analytics, for example, will lead to an inconsistent, half-hearted and ad hoc approach, meaning SMBs will lack the foresight to survive in an increasingly digitalised economy.

To address this, TrueCue has designed a comprehensive data and analytics maturity framework for SMBs looking to achieve more inclusive, flexible and trustworthy business outcomes from their data. In part three of this series of articles, we dive deeper into the framework, looking at two of the six key areas organisations must address to achieve high-level analytics maturity.

Strategy

The first category on TrueCue’s maturity framework is strategy. The transformative power of data can lead to immeasurable benefits and this begins with defining a clear data and analytics strategy that aligns closely with the core business goals.

To measure its maturity level, the category is assessed across four areas: sponsorship, alignment to corporate strategy, data and analytics roadmaps, and benefits tracking. Based on where SMBs score in each area, analytics maturity is classified on a scale from one to five, with five suggesting the business is highly advanced across the board.

So, let’s unpacks these terms in greater detail:

1. Sponsorship

A sponsor is the person or team that drives the adoption of data and analytics practices throughout the business. For many companies, sponsorship often stems from the IT department, leading to analytics being used on a case-by-case basis.

Before any real advancements can be made, SMBs must switch to the “discover and descend” method, where direction, momentum and financial sponsorship come from  digitally literate decision makers at the top. For example, business leaders that use dashboards to drive conversations at board level will see this spirit flow throughout the entire organisation.

Without strong leadership, even the most robust strategy will likely run into cultural resistance and structural bottlenecks.

2. Alignment to corporate strategy

By failing to align data and analytics strategies to broader corporate strategies, many SMBs fall short of achieving maximum business value from their investments.

For example, it is common for organisations to focus initially on data acquisition and management, without stopping to consider how the data will be used. There can also be friction around ownership of analytics initiatives, with data being leveraged in different ways by different teams and creating inconsistent insights.

To combat this, organisations must ask themselves what is at the heart of the business and what new doors data can open. For SMBs looking to drive operational excellence and product innovation, having a data and analytics strategy that is symbiotic to the business itself will be critical.

3. Data and analytics roadmaps

For any strategy, there is always going to be a complex web of day-to-day tasks and variables to manage, including hardware acquisition, training, governance, data management and visualisation. A clear roadmap of where SMBs are going with milestones of how to get there is therefore crucial.

This is where advanced planning comes into play, by translating interdependencies into a clear, actionable plan. Mature analytics strategies will also investigate how investments will be sequenced and ensure co-ordination between IT departments and analytics teams. Without a clearly articulated roadmap, even a simple strategy can lead to unrealised goals.

4. Benefits tracking

Finally, what is the point of establishing a successful strategy if SMBs cannot track the benefits? In fact, benefits tracking not only provides insights into the financial and non-financial value that has been delivered, but also what could have worked better. There is always room for improvement and mature benefits tracking will highlight this.

A prospective take on benefits tracking also enables organisations to plan investments ahead of time and reduce potential waste. SMBs must therefore prospectively identify which benefits to measure, while also gaining a retrospective overview of the project to link any future reinvestments to fully realised benefits.

A common downfall in this area stems from tracking qualitative insights, such as changes in decision-making culture, as they are harder to measure with traditional means.

Process

The second category is process – the methodology behind the management and documentation of analytics demand. Without high-level processes in place to drive the use of analytics across an organisation, the true value of data cannot be recognised.

This category is also assessed across four areas: demand pipeline, reporting lifecycle management, business processes, and adoption, on a scale of one to five.

1. Demand pipeline

A demand pipeline can be defined as the processes in place to filter, prioritise and fund incoming analytics requests, and is the first step in transforming data into tangible value. Once a successful strategy is in place, analytics teams will start fielding requests from all areas of the business and this requires a purposeful and focused management approach.

For example, certain analytics projects will require additional data, which can lengthen the project timeline. SMBs must therefore carefully consider the quality and availability of data when managing requests. Regular and frequent processes will also be crucial to ensure the buy-in of data throughout the entire organisation.

2. Reporting lifecycle management

If data is fresh, insights are likely to be more valuable. An effective lifecycle reporting structure which removes unused data and continuously reviews the most appropriate data sources must be prioritised.

In top-level SMBs, processes and automation will allow for cohesive, standardised reporting in a unified fashion. Navigation of dashboards can also be difficult, so an intuitive and structured user interface will be necessary.

Organisations with poor lifecycle management structures are likely to suffer from “grey area” reporting, where a lack of reliable data forces employees to create their own interpretations, which are often inaccurate.

3. Business processes

In any SMB, understanding how the business operates is critical to survival, and maintaining this understanding when managing data is no exception. SMBs with a clear vision of business processes, alongside documentation that identities where bottlenecks might arise, will benefit from stronger, more informed decisions.

Organisations looking to achieve high-level analytics maturity must therefore conduct an audit to identify the different tasks and stages of each process. Communication and understanding of the importance of focused business processes can mean the difference between an aimless and successful analytics approach.

4. Adoption

Analytics insights that are not leveraged are ultimately worthless, meaning a formal and structured approach to adoption is a crucial step in becoming a data-driven SMB.

The first thing to consider is involving end-users in the development and build process. By contributing to the construction of the processes, they can offer insight into what kind of data will best serve them. Creating detailed user guides supported by on-demand videos or gifs will also set SMBs apart from their competitors.

Without high-level adoption processes, embedding the use of data and analytics into an organisation’s DNA will prove difficult.

As an increasing number of SMBs look towards the power of data to reengineer their decision-making, advance their daily operations and plan ahead for an uncertain future, robust data strategies and high-level processes are the foundations upon which successful ambitions are built.

If you want to learn more about becoming a data-driven SMB, get in touch with us directly to discuss how you can begin transforming the business.

The next article in this five-part series will focus on data, culture and skills