An actionable guide for small and medium-sized businesses on how to deliver the right platform solutions for their data and analytics.


In the 21st century, data is an increasingly coveted resource. Businesses around the globe are in the process of re-orientating to a data and analytics driven approach.

Recent research shows that 82% of CEOs are taking steps to transition toward digital operations (a 20% increase from two years ago) and 77% are planning to increase their investments in digital capabilities.

Tie Your Data and Analytics Initiatives to Stakeholders and Their Business Goals. Gartner, 2020

The ready availability of powerful data collection and analysis tools means that businesses of all sizes can now access valuable, information-driven insights once reserved for the biggest and most well-resourced corporations. Small and medium-sized businesses (SMBs) are using these advancements to drastically improve or even transform the way they do business. 

Tools and technology are only part of the picture – digital natives already view data and analytics as both an asset and intrinsically linked to overall business strategy. However, too many organisations still take an inconsistent, half-hearted or ad hoc approach and, in doing so, fail to maximise their possible benefits. 

Truly harnessing the potential of data and analytics requires a fresh perspective on its importance as a resource, a fundamental re-evaluation of its role in executive-level decision making, and a clear strategy. 

Platforms - Data and Analytics Maturity Framework

We’ve created our data and analytics maturity framework to provide SMBs with insights on the core competencies needed for successful digital business. 

Our framework is separated into six categories:

  • Strategy
  • Process
  • Platforms
  • Data
  • Analysis
  • Culture and Skills
How Data-Driven is your organisation

This guide looks at the role platforms play in supporting your data and analytics strategy. We break this topic down into four essential subcategories:

Platforms enable your vision. They facilitate the reception, processing and presenting of data for analysis. However, it’s not just what your platforms can do, it also matters how they do it. Efficiency, security, robustness and data government capabilities are all important factors in delivering the right platform solution for your data and analytics objectives.

No matter the focus, size, or scope of your data and analytics strategy, accessing the right toolset at the right time is pivotal to success.

So, let’s examine the different aspects of data and analytics platforms in detail.

1. Storage and Management

The ability to efficiently, and securely, house your data assets is a cornerstone of any effective data and analytics operation.

It covers the ability to store your data in the manner required for your analytics objectives, and to provide access for your teams whilst adhering to the security and governance policies of your organisation.

SMBs taking steps to progress their data storage and management maturity should start by taking a careful look at the specific needs and goals of their business. While it’s never been easier for SMBs to access the powerful solutions traditionally reserved for large enterprises, most organisations do not need the whole set of services.

What types of data are you working with? How will it be used and to what ultimate purpose? You might be looking at something as straight-forward as file storage and databases, or perhaps you’ll be handling more advanced forms of data such as streaming logs, or audio and video files. Each scenario demands different technologies and approaches to storage and management.

It’s important to concentrate on the areas that provide value, and a thorough examination of your data and analytics strategy is prudent before rushing to make any large investments.

Issues of data security and governance raise further questions. Data is both a valuable resource and, potentially, highly sensitive. Inconsistent or underdeveloped security and governance practices can all too easily turn assets into liabilities. Investments in data storage must also be paired with robust security and governance polices to manage any potential risks.

Businesses of all sizes are increasingly looking to the advantages offered by cloud-based solutions for their data storage and management operations. Gartner research shows that on-premise data storage is decreasing and that the majority of growth (68%) in this area is coming from the cloud.1

It’s not hard to see why: cloud-based services have the flexibility to adapt and scale with your businesses short, medium and long-term data and analytics strategy. Storage and compute resources, Azure cloud analytics services and integrated security safeguards are helping SMBs quickly achieve levels of data maturity that previously would have taken years of gradual investment.

Want to assess the data and analytics maturity of your organisation?

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2. Data Preparation

The foundation of any analytics project is the data itself. Accurate data leads to good decisions – however, if the information contains errors, is incomplete or out of date, then any future actions based on it may be fatally flawed.

Data preparation is the process of cleaning, correcting, shaping and modelling information into a form that is fit for analysis. It is an essential step to extracting value out of the raw data your business receives.

“Data quality has been recognized as a key component of any data and analytics strategy for over 20 years.”

Data Quality Fundamentals for Data and Analytics Technical Professionals. Gartner, 2020

This is why cleaning data, and ensuring its quality, is such an important stage of preparing data for analysis. The process can involve removing incorrect data, finding and filling data gaps, or removing duplicated entries.

The practical upside to this time-intensive practice can be far reaching. Not only does quality data play a crucial role in planning and decision making, it makes data easier to map out and store, and ultimately reduces the inefficiencies that bad information can cause.

Another key stage is the shaping and formatting of data for different analytical purposes. Data that is modelled using consistent, organisation-wide methodologies can make it much easier in the long run for teams to search, identify and access the data they need to drive business value.

Of course, not all data requires the same level of preparation. The amounts and types of data that your analytics team uses will determine the appropriate level of investment. However, data preparation is never an area to leave under-resourced.

It’s not uncommon for companies to end up with ad hoc solutions that have evolved over time. While this approach can remain functional at a small scale, it has a definite ceiling. As a data and analytics strategy grows in size and ambition, so does the complexity. Without a standardised approach, separate teams may process data differently, use different tools, and possibly even reach different conclusions.

SMBs looking to extract the full value out of their data sources, should look toward growing their data maturity and standardising their approach to data preparation. This could be as simple as developing a consistent methodology for existing tools or, depending on the type of business and the ambitions of the data and analytics strategy, a more fundamental re-evaluation may be needed.

It could be necessary to regularly review current tools for organisation-wide suitability, and to re-evaluate the requirement for all data sources. Frequently, multiple capabilities may be needed to fully capitalise on different data sources and flexible cloud services should be investigated as a potential way to support this.

3. Metadata Management

Metadata management provides information about your data assets – allowing them to be used effectively. It is often regarded as a specialist capability that only mature data organisations can achieve, and it’s true that many metadata management software tools are focused on enterprise organisations only.

However, it would be a mistake to ignore the issue of metadata management as irrelevant to SMB’s. Metadata management is not a one-size-fits-all solution and can in fact bring many benefits to smaller-scale data and analytics operations. Metadata can provide organisations with a clear overview of what data is available to them, where it came from, and how it will be used. This improves efficiency, consistency and data-driven decision making.

One core activity in metadata management is data cataloguing. It provides a comprehensive inventory of your data assets, organised into logical categories. Having a well-maintained inventory increases the accessibility of your data by making it easily searchable across the organisation. It cuts down the time it takes to select data for analysis and makes it easier to reuse data for different purposes, maximising the value of each data source.

Data lineage, examines the life-cycle of your data. Where it came from, how it was prepared and processed, where it ended up and how it was eventually used. This can be especially useful when refining your internal data and analytics processes and for measuring the benefits of individual data and analytics initiatives. Data lineage also play an essential role in risk management, as it eliminates any ambiguity surrounding your data assets, and how they are prepared and calculated.

Master data management ensures consistency and uniformity of your data assets across the organisation. As a data and analytics initiative expands in scope, it is all too easy for discrepancies to occur as different teams process and utilise data. Master data management ensures that there is a single version of truth across the organisation for common sets of data.

Metadata management is still a relatively small practice in many organisations. According to Gartner, only 12% of data management activities are currently dedicated to metadata management. However, Gartner also predicts that by 2022, organisations utilising active metadata to dynamically connect, optimise and automate data integration processes will reduce time to data delivery by 30%.2

Again, these capabilities are more often seen in more mature data organisations. However, every organisation needs to consider their own data requirements if they are to effectively derive value from their data assets.

4. End-user tools

End-user tools are software applications that allow teams across an organisation to analyse, interact with, and extract value from data assets.

These tools can be used for business intelligence (BI), for ad hoc analytics, and for reporting. They can also be used to help measure and streamline internal processes by running data science experiments or interrogating databases.

When it comes to end-user tools, it’s not uncommon for businesses to rely on ad hoc, or ‘make-do’ solutions for far longer than is practical. Excel is often used for this purpose in small and medium-size businesses, and of course, with enough know-how, it can be customised to support a range of reporting functionalities. However, in the long-term this can create a critical dependence on individual specialists, which in turn can lead to bottlenecks and change-averse behaviour. As data and analytics initiatives grow in size and complexity, the need for built-for-purpose software tools becomes more apparent.

As always, any SMB looking to change or add to their end-user toolset should begin with a close examination of the business needs and overall strategy before any investments take place.

What existing tools do you have? Are they sufficient or is change needed? If change is needed, what is driving that change?

It’s important to define the requirements across teams and decide on a consistent set of data tools. Having a consistent toolset reduces training requirements across the organisation and helps maintain data governance and security policies. It will also help accelerate capabilities across preparation and reporting of analysis.

Another key consideration for end-user tools is usability. Your toolset is fundamental to empowering your analytics team, but is also hugely important in shaping how the wider organisation engages with data-driven decision making.

High accessibility end-user tools promote more widespread data usage on a day-to-day basis. It allows non-technical business users to effectively collaborate, use, and gain insights from your data assets. This in turn speeds the data maturity of your organisation.

Increasingly, data mature organisations are looking to modern BI tools provide teams with the right tools, for the right job, with minimal configuration. Gartner defines modern BI tools as characterisd by agility, flexibility and ease of use throughout every phase of the analytics workflow.3

This flexibility is transforming the way businesses utilise their data. Self-service analytics and cloud based services can remove many of the technical hurdles seen in traditional reporting tools, allowing data-driven decision making to permeate the business as a whole.

If your organisation is ready to be transformed by data and analytics, then check out the maturity framework or contact TrueCue directly to have a chat and find out exactly what the business needs to succeed.


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