This is the fifth 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
Data is quickly changing the way we operate, helping organisations to improve their bottom-lines, gain agility and spot meaningful innovations. However, by failing to consider the six key competencies required to leverage data correctly, many small and medium-sized businesses (SMBs) risk falling foul to low-level data and analytics maturity.
These six key areas are strategy, data, process, culture and skills, analysis and platforms, with each area interdependent to the others and of equal importance to gaining maximum business value from data. As explored throughout this series, excelling in all areas will be critical for organisations looking to transform into a data-driven business and weather the storm of the ever-evolving digital landscape.
In the final article in our Data-Driven SMB series, we dive deeper into the final two areas of TrueCue’s six-part data and analytics maturity framework: analysis and platforms. Achieving high-level analytics maturity not only includes a fresh perspective on data as a critical resource, but also a fundamental evaluation of how data can meet overarching business objectives using the correct platform solution.
So, let’s examine the different dimensions of analysis and platforms in detail.
Analysis
Once data is refined and governed to a sufficient level, business leaders must consider how it is going to be analysed. In the most data-mature organisations, analysis will enable the visualisation and interpretation of data in a way that can be easily understood across the business. After all, what is the point of identifying relevant data sources if you can’t extract full value from them?
To measure the maturity level of this category, analysis is assessed across four dimensions: sophistication, automation, dissemination, and permissions and access. Based on where businesses score in each area, data and analytics maturity is classified on a level from one to five, with five suggesting the organisation demonstrates maturity across the board.
1. Sophistication
Sophistication is the complexity of analysis across an organisation, and finessing this can often be a long journey. Business leaders must therefore recognise that jumping ahead can risk putting analysis on weak foundations.
To achieve the right level of sophistication, it is best to first identify the decision-making processes business leaders aspire to implement, and then decide on the steps that can improve these processes. This will also help to ensure the analysis is deep enough to meet overarching objectives.
Organisations that operate at the most sophisticated level of analysis will benefit from self-learning analytics, AI and machine learning, which will generate valuable foresight across the business.
2. Automation
Analysis can quickly become outdated when relying on individuals to manually check, prepare and present data. The automation of processes is therefore paramount to success, particularly with growing issues around business continuity.
Organisations looking to weather the storm must develop a data warehouse to automate processes such as extracting, transforming and loading data. This, in turn, will reduce manual efforts and free up analysts’ time to perform data analysis rather than data processing.
In the most data-mature businesses, data science projects and machine learning are present, with AI being leveraged to automate processes in certain cases.
3. Dissemination
Whether purposefully or not, individuals store data on their machines due to uncertainty around security breaches or modern platforms. This can lead to multiple truths and friction over what data sets are accurate.
To combat this, business leaders must prioritise dissemination, better known as sharing. For example, data-mature organisations will foster a culture of collaboration and share analysis across different departments.
Scalable, browser-based analytics platforms and self-service analytics, such as interactive dashboards, should also be used by all and be accessible via mobile.
4. Permissions and access
Widespread sharing of analysis can often raise questions around security. These concerns can be managed, however, by understanding permission and access.
Organisations must enable access to the right data at the right time (permission) and store data somewhere that cannot be hacked (access). Methods such as two-factor authentication (2FA) can offer enhanced security. For example, if data is tied to a particular individual, 2FA is an effective way to ensure their password has not been compromised.
Businesses that excel in this area will benefit from the highest levels of data encryption, with security centralised across all applications and controlled at user level.
To learn more about how to improve the maturity of your organisation’s analysis, check-out TrueCue’s Guide to Analysis.
Platforms
Delivering the correct platform solutions for data and analytics objectives is pivotal to becoming data-mature. Not only do these solutions ensure data is stored and managed in an efficient manner, but also facilitate advanced data analysis. Organisations must therefore ensure they select the right data platform for their needs.
This category is also assessed across four dimensions: store and management, data preparation, metadata management and end-user tools, on a level from one to five.
1. Store and management
Housing data assets securely is a cornerstone of achieving data and analytics maturity. Businesses looking to transform their operations must therefore ask themselves: what types of data are we working with? How will it be used?
From straightforward file storage to more advanced data such as streaming logs or video files, concentrating on areas that provide most value is critical. Investments in data storage must also be paired with robust security and governance polices to manage potential risks.
It is important to note here that businesses of all sizes are looking to cloud-based solutions for data storage, which offer greater flexibility to adapt and scale.
2. Data preparation
Data preparation is the process of cleaning, shaping and modelling information for analysis, and so a platform’s ability to action this in line with an organisation’s data strategy is key.
Businesses should therefore look to solutions that standardise their approach to data preparation, by using all integrated tools fully and automatically. For example, platforms will often develop consistent, organisation-wide methodologies to identify data needed to drive business value.
Higher-level platforms will also regularly review preparation tools for organisation-wide suitability and incorporate new data sources where appropriate.
3. Metadata management
Metadata management provides unique information about data assets and is often regarded as a specialist capability that only enterprise organisations can achieve.
However, it would be a mistake to label this dimension as irrelevant to SMBs as metadata management is not a one-size-fits-all solution. In fact, not only can it provide an overview of what data is available, but also where it came from and how it will be used – with core activities at this stage including data cataloguing and lineage.
In the most data-mature organisations, metadata management is aligned and governed across the business, thereby ensuring the consistency and uniformity of data assets.
4. End-user tools
Finally, when determining what platform best suits their needs, organisations must consider the end-user tools available. At this level, many businesses often use ad hoc (“make-do”) solutions for far longer than is practical and this must change.
Ultimately, advanced end-user tools can help to streamline internal processes, reduce training requirements and maintain data governance. Businesses must therefore choose the correct tools that fit their overarching objectives and define requirements across teams.
Increasingly, data-mature organisations are looking to modern BI tools to provide teams with the right equipment for the right job, with minimal configuration.
To learn more about how to select the right platforms to help improve your organisation’s data and analytics maturity, check-out TrueCue’s Platforms Guide.
There is no single-shot solution to truly harness the potential of data. Organisations looking to remain resilient in an increasingly digitalised world will therefore need to gain a clear understanding of their data and analytics maturity, and the six core areas needed to excel across the board. With raised expectations for businesses to justify their decision-making processes, transform their operations and advance their digital capabilities, prioritising the areas that need immediate improvement will mean the difference between sinking or swimming.
Our framework offers business leaders a simple way to measure and compare their current level of data and analytics maturity, enabling them to navigate the next stage on their journey to becoming data-mature. If you want to learn more about the benefits of becoming data-driven, contact us directly to discuss how you can begin transforming your business.
If you want to learn more about becoming a data-driven SMB, contact us directly to discuss how you can begin transforming your business.
| This is the fifth article in a five-part series 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 4: The Data-Driven SMB Part 4: data, culture and skills |
