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:
- Culture and Skills
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.
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.
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.