Analysis

An actionable guide for small and medium-sized businesses on how to achieve greater maturity in your analysis.

Introduction

Building a clear pathway to data maturity, using the TrueCue data and analytics maturity framework, can mean understanding the importance of practices around data that SMBs (small and medium-sized businesses) might never have seriously considered.

Our framework that can be used to help build data and analytics capability is made up of six categories:

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

To become a truly mature, data-driven company, achieving greater maturity in analysis is essential.

If data is all the things it takes to make a painting (paints, brush, canvas etc.) then analysis is the artist using those components to create a picture. Proper analysis of data allows organisations to visualise and interpret data in a way that can be understood. This can help implement better data-driven decision making.

Between 60% and 73% of all data within an enterprise goes unused for analytics” and, when an organisation did attempt data and analytics projects, only 27% produced actionable insights.”

Closing the Data-Value Gap – How to become data-driven and pivot to the new. Accenture, 2019

Despite strong analysis having the potential to bring huge value to those who are advanced with their strategy, it is clear that the analysis of data has not been deployed across the board by small and medium-sized businesses.

If organisations boost their use of analysis, they can better use the data at their disposal and gain a significant edge against the competition.

Using the data and analytics maturity framework, an organisation’s maturity in these dimensions falls into five levels. To fully understand and exploit analysis effectively, it’s helpful to learn what each dimension is about and what level your business stands at.

1. Sophistication

SMBs are often stifled by the platforms at their disposal and, according to Gartner’s IT Score for data and analytics survey, 87.5% of respondents are classed as “low maturity.”1 For an SMB with low maturity, analytics will not be widely understood and will not result in gaining much more insight than they would’ve done five to ten years previous.

Each step an organisation takes in the maturity journey is vital and jumping ahead to the most sophisticated form of analysis risks putting that analysis on weak foundations. This could hamper data-driven decision-making in the future.

To achieve the right sophistication for the organisation, it is better to identify the decision-making processes they aspire to implement. Once that has been the done, they can decide what steps are needed to improve these processes.

A desire to employ advanced AI analytics is correct if the platforms can be built to enable that. If sophisticated analytical techniques need to be commonplace in the company then it is worth pursuing. Otherwise, money and time are better suited being used in other areas to improve maturity.

High expectations don’t have to be met immediately. Just the act of basic reporting and organising data so that business performance can be tracked, begins the journey to clean and reliable data. This step is critical to leveraging advanced analytics using AI. This simple step can help organisations climb out of a low data maturity rut, though there are many other steps out there to take.

The challenge around analysis is making sure that it is deep enough to help businesses answer the questions they‘ve been asking. The information also needs to be presented in an easy-to-understand way, without losing any of the insights that may have been found.

As the analysis becomes more complex, a greater volume of data needs to be analysed and a wider range of sources and sets need to be linked together. Acquiring and managing these disparate data sets can be done through systems, such as a company data warehouse, easing the process.

Which leads to automation.

2. Automation

After an unprecedented year for everyone, Gartner has reported that, in 2021, data and analytics and AI will be at the forefront of organizational efforts to turn crisis into opportunity and recovery.2

The COVID-19 pandemic has caused business continuity issues that have highlighted and underlined a clear need for automation. More than ever, data analysis can quickly become out of date when it relies on individuals to prepare data and check the results before publishing the analysis.

The slow nature of this can sometimes lead people to not trust the insights gained from the analysis because they are out of date. Or even ignoring them completely.

These manual, human led business practices remain in place due to unfounded fears of AI. Automation within analytics isn’t about robots turning up and taking jobs. It’s about helping tell stories with the data that has been collected, and removing repetitive, non-value add activities that are currently being done by a skilled workforce.

Understanding the need for automation and taking the steps towards more advanced analytics will help the insights become nuanced and human.

A huge part of automation is that it saves time, freeing up your analysts to perform data and analytics, rather than data processing. If an SMB develops a data warehouse to automate key processes such as extracting, transforming and loading data into the centralised warehouse then manual effort can be greatly reduced. This can save hundreds of work hours in the future—time that can then be spent on value add activities and storytelling with the data.

Another common misconception is confusing automation with repeatability. In SMBs with low maturity, repeatable processes and workflows can be created but will often require manual management. To get these processes to run with little manual intervention and to produce analysis for the organisation that can be used effectively is another challenge entirely.

These goals of near complete automation are not often achievable for those least data savvy businesses. Before an organisation can reach automated analytics, they need to build up the skill base and knowledge to make it happen. Investing in human and technical structures can ease these periods of transition as an organisation rises up the maturity stages.

If the entire business, from CEO to office worker, understands this knowledge then it will provide a greater skill sharing approach across the organisation. This approach will allow the organisation to reach their maturity expectations.

The idea of sharing may seem glib or childish, but it is a core value to establish. It’s also worth noting that when it comes to analysis, sharing is better known as dissemination.

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3. Dissemination

The sad fact of life is that people love to hoard data.

Whether innocently or not, individuals keep data store on their machine that may help inform business decisions. They do this for fear of security breach, or that it is not capable of working with modern platforms. This leads to multiple versions of the truth, and sometimes arguments and infighting as to which data set is correct and accurate. To gain greater maturity in analysis, a culture of collaboration and willingness to share across desks and departments is fundamental.

Even if this culture of collaboration and the widespread dissemination of data is achieved, there are still other dissemination related challenges to overcome.

In an SMB at maturity level 1, there are multiple versions of the same content all over the organisation. Spreadsheets, reports and documents are tweaked, updated and then not shared widely again. Most of the time, this work is carried out on individuals’ laptops and then sometimes emailed to their colleagues.

All over the organisation the exact same metrics can be analysed in any number of ways. This can sometimes lead to conflicting insights and ideas on which paths to take. If an organisation is conflicted on its path forward, then it is hard to make decisions that will support the journey.

To build consistency from the data collected and analysed, it might be the right decision to create a CoE (Centre of Excellence.) A CoE is a team of experts within the organisation that is already familiar with the data sources available. This team can streamline the analytic efforts and steer everyone in the right direction, getting the most value out of the resources at hand.

Putting a CoE together and bringing all the data into a central repository, such as a data warehouse, will lead to a better culture of consistent sharing of information.

If widespread sharing of data and analysis is achieved, the question of security comes into play. If there are no company rules about distributing data, then key analysis can be shared, lost or stolen. Without controls an organisation may not know who has what information and what has been shared.

This concern can be managed by understanding permission and access.

4. Permission and Access

The dimension of permission and access is closely linked to dissemination.

As stated above, teams and individuals often produce their own data analysis due to ownership of the data. When data is shared it is often done insecurely. The real fear of having data stolen or lost can result in people becoming less open to sharing, because security is not in place.

Because of this, data and analytics is often held in silos—raw data that is held by a one department and isolated. Due to a lack of security, there is no governed way to share information and access control is basic and manual.

For better maturity, SMBs need to control giving the right access to data at the right time (permission) and secure the data somewhere that cannot be hacked or maliciously used (access).

Businesses are starting to confront the issues around permission and access by utilising the cloud and according to Gartner, by 2022, public cloud services will be essential for 90% of data and analytics innovation.3

Modern platforms and tools, such as Microsoft Azure, have the capabilities to securely share analysis but are not exploited.

Securing data on any analytics project is paramount. Aligning the permission and access with the source systems in use can be a safe way to store and share important data.

Methods such as 2FA (two-step authentication) can offer security for data. If, for example, data is tied to a particular individual or role, then 2FA is an effective way to ensure that the password or device accessing the data has not been compromised.

This is becoming even more important with the rise in remote working, where the physical security of the device is often outside the organisation’s control.

Sharing data is important to driving up maturity. The aim for a modern, data-driven company is to do it in a secure way. If people trust the security of their sharing, then they will do it often and without prejudice.

At the centre of a data-driven decision-making SMB is an understanding of analysis. Without a good painter, the picture an organisation makes with the tools on hand will come out unfocused, or just plain bad. The modern age has brought in ever changing technology that has never been more accessible to SMBs and the resources to educate from board level to office worker are abundant.

Concentrating on understanding and building these four dimensions into an analysis strategy will boost an organisation’s data analytics maturity. Combining these practices with the other categories in TrueCue’s data and analytics maturity framework will help in business performance and provide a better platform to succeed.

Analysis is just one part of the puzzle. The data and analytics maturity framework is made up of five other categories: strategy, process, data, platforms and culture & skills.

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.

The Data-Driven SMB

An actionable guide for small and medium-sized businesses, on how to become more data-driven in an increasingly digitalised economy.

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