This is the fourth 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
As explored throughout this series, there are six key areas small and medium-sized businesses (SMBs) must address if they are to achieve a higher level of data and analytics maturity. For many organisations, this journey will be pivotal to ensuring stability and survival at this challenging time.
However, as previously discussed, by simply investing in technology and failing to drive adoption of analytics throughout the entire business, SMBs are likely to face cultural resistance. This disruption, in turn, will lead to an inconsistent and ad hoc approach to data and analytics, resulting in organisations falling short of gaining truth and value from their data.
In order for any business to transform their data into meaningful business value, they must first consider how they source, integrate, validate and govern multiple data sources, securely. Business leaders must also set the precedent, and instil a culture of data and analytics across the organisation, ensuring employees have the required skillsets to generate trusted insights, and are encouraged, inspired and supported to embed data and analytics into everything they do.
With this in mind, we’ll unpack the “data” and “culture and skills” categories of TrueCue’s data and analytics maturity framework. For SMBs looking to advance their digital capabilities and survive in an increasingly digitalised economy, data must be available, validated, understood and supported at all levels.
The most obvious category SMBs must address when looking to achieve advanced analytics maturity is data. To be able to analyse data effectively to generate stronger, more informed decisions, it is best to start with the basics, by ensuring data is varied, refined and governed to the correct level required for the specific analysis.
To measure the maturity level of this category, data is assessed across three dimensions: data variety and sources, data availability, and data cleansing and governance. An organisation’s score in each area, data and analytics maturity is classified on a level from one to five, with five suggesting the corporation excels in all areas.
1. Data variety and sources
It is important to remember that internal, external, structured and unstructured data is all useful. Information is incredibly diverse and there is no one-size-fits-all solution. Organisations must therefore tread carefully when identifying relevant data sources and extract full value from all available data, regardless of its type.
For example, in recent years, social media has become a powerful source of unstructured consumer data, responsible for thousands of successful marketing campaigns. Therefore, while dealing with structured data is a good starting point, SMBs must take this to another level and ensure a variety of data sources are being used to drive better decision-making
2. Data availability
SMBs must help themselves by deciding what data needs to be collected and identifying potential gaps from the onset. The bottom line is that data needs to be readily available in order to generate value for a business.
Data availability can mean different things to different businesses. Start-ups, for example, will focus on identifying, documenting and sharing data, whereas more established organisations will consider the automation of data preparation processes.
Organisations looking to transition to higher stages of data and analytics maturity often struggle to create mechanisms for collaboration and end up storing data in departmental silos. Breaking these down and ensuring correct data models are used across the business will be critical to building a smarter, common pool of data.
3. Data cleansing and governance
As the variety and availability of data sources increase, so does the task of managing and processing these. This is where data cleansing and governance come into play. Businesses need to ensure there are clear rules in place for the handling, storing and maintaining of data, while determining accountability for the quality of overall data sets.
A common issue at this stage is data quality being sufficient enough for an individual’s purpose, meaning there is no incentive to improve this for organisation-wide analytics. SMBs with poor data cleansing methods can also fall foul to bad data such as corrupted files, incomplete data sets or duplicates.
For organisations looking to transition to a more efficient method of data governance and cleansing, having a clear plan that includes milestones of how to meet objectives is key.
Culture and skills
Once the tools and technologies are in place to extract data seamlessly, business leaders must review how data and analytics is conducted and viewed across their organisation. A clash between data culture and organisational culture will lead to friction and inconsistency, while limited or non-existent skillsets will mean employees fall short of achieving basic requirements.
This category is assessed across four dimensions: community, skills, learning and development, and best practices and standards, on a level of one to five.
Effective analytics is not a “single-shot” solution, but an ongoing process of development, where employees learn how to interpret, understand and implement data-driven decisions on a continual basis. Creating a structured community, based on a culture of learning and sharing, is therefore paramount to achieving high-level data and analytics maturity.
In the most data-mature SMBs, structured community activities occur on a regular basis, with widespread awareness of the importance of data-driven insights. Activities will also be driven by defined community roles from both inside and outside of the business, with external experts offering unique perspectives.
The relevant skillsets available within an organisation, including soft and technical skills, must also be considered. Teams looking to manage data efficiently will need to be knowledgeable, self-sufficient and sustainable, calling for SMBs to establish a common baseline of understanding to conduct data and analytics successfully.
Organisations with high-level analytics maturity will therefore reward employees for developing skills and, in turn, benefit from analysts who are proficient across multiple platforms, including data science and AI. Skillsets should also be seen as ever-evolving – whatever your level of experience, there is always scope to learn more.
3. Learning and development
Following on from skills, efficient learning and development (L&D) practices must be established to ensure appropriate skillsets are available to leverage data on an ongoing basis. SMBs must therefore invest both time and money in L&D to ensure it becomes an integral part of their community, rather than an optional extra.
Organisations who excel in this area will offer regular analytics learning programmes for analysts and on-demand training for employees across all levels and specialisations. This includes structured training courses that targets skills, as well as conferences, roadshows and webinars from external providers.
4. Best practices and standards
And finally, for SMBs looking to become data-driven across the board, best practices and standards to guide communities in carrying out their work is critical. This means having documentation and processes in place to offer continuity throughout the organisation, rather than a prescriptive set of right and wrong.
For SMBs looking to develop these processes, a sensible first step is developing style guides, including best practice examples. The possibilities in analytics are endless and without a guide in place, organisations risk data being presented inconsistently.
That said, it is important to bear in mind that strict rules can sometimes stifle creativity. Organisations must also be sure to allow employees room to think outside the box.
By following TrueCue’s maturity framework, SMBs can ensure they are using their time and investments wisely, by prioritising the relevant dimensions and establishing advanced data and analytics maturity across the entire organisation.
If you want to learn more about becoming a data-driven SMB, contact us directly to discuss how you can begin transforming your business.
|The next article in this five-part series will focus on analysis and platforms.|
Part 1: The Data-Driven SMB
Part 2: The Data-Driven SMB Part II: The Data and Analytics Maturity Framework for SMBs
Part 3: Data and Analytics Maturity Framework: Focus on Strategy and Process
Part 5: Data and Analytics Maturity Framework: Focus on Analysis and Culture & Skills
Release Date – 16/02/21