March 25, 2021

4 key considerations to advance beyond a low data maturity rut

Tim Archer
Tim Archer Director of Analytics

Business challenge:

Data and Analytics Roadmap
Data and Analytics Strategy
Data Maturity

With 87.5% of mid-sized organisations classified as “low data and analytics maturity”, SMBs (small and medium-sized businesses) can often find themselves stuck in a rut, and unable to advance with their maturity. 1

Whilst an increasing number of SMBs are showing the ambition to push their organisation forwards, many remain unsure as to how they should implement a business-wide data and analytics strategy. 

Despite this uncertainty, they are perfectly placed to drive change and make the most of data and analytics, as a primary driver of business value. Their small, agile nature is often the perfect profile to make the change and realise the benefits of becoming a data-driven organisation.  

Below are four considerations that may help your organisation to progress out of a low data maturity rut. 

1. Set Achievable Goals 

To set yourself up for success you need to initially narrow your focus. Looking at the big picture of optimum data maturity may result in disillusionment, it’s important to remember that success does not occur overnight. 

Creating a roadmap to address and fix the basics will generate greater analytics support from business leaders. Providing quick wins in analytics and targeting areas that can be easily improved will help to further create momentum.

For example, an SMB with low maturity could reduce the number of spreadsheets being used to report data and integrate data sources. This will help produce more clean and reliable data and remove manual processes which are inherent when it comes to managing data in spreadsheets. A small step but an important one. 

Simple automation of analytics could also be employed for quick success – for instance, a company might automate sales forecasts that were done manually. 

The key to continuous improvement is setting goals that move your maturity forward and achieving them – bringing business outcome focus and becoming task driven is key. 

2. Encourage Collaboration 

Building a culture of cross department collaboration and acceptance of data analytics will have a long-term effect on its use in a business. 

Analytics leaders should work closely with other business leaders to develop an organisation wide strategy to achieve better maturity. This collaboration between departments should be a continuous process that is always evolving to suit the business goals of the organisation. 

Making use of different perspectives will also result in better data and analytics, and therefore maturity. Involving business stakeholders in analytics dialogues may lead to considerations that had yet to be explored, which in turn produces more robust analysis. 

A low maturity SMB analytics team could talk to a sales manager to better understand how orders are populated and processed by the department. Too often analytics is carried out in isolation to the intended target audiences, this only alienates them and introduces uncertainty. Using the specific knowledge of business teams and leaders, the analytics teams can mine the knowledge and natural analytical capabilities related to these business roles. 

3. Learn Together 

For an organisation to get out of a data rut, everyone in it must embark on the journey and be ready to learn and broaden their own horizons. 

When an organisation attempts to implement more advanced data and analytics projects, the lack of skills amongst many of the workforce can become evident. Having data and analytics leaders provide ongoing support will result in companywide upskilling and learning, leading the business towards greater data maturity. 

Mixing up training delivery methods by taking note of skill differences, the free time people have and the role they perform will improve learning effectiveness. Keeping note of progress across the organisation and gamifying some of the training will help create a greater sense of community

4. Realistic Investment 

When it comes to advancing the organisation out of low data maturity rut, it’s about where the money goes – not always how much. 

Organisations with limited resources and a shortage of analytics talent could consider analytic applications that are able to generate fast results, but may not be as broad as other products. Perhaps turning to service providers that offer implementation services and not just the application will result in an easier transition. 

With the analytics strategy, employing KPIs (key performance indictors) will allow the business to see the progress of implementation. Using KPIs, the benefits will be clearer, informing the business decisions around investment and judge the success of the strategy. 

Providing justification by evaluating the cost, benefits and risks of analytics projects will help the business leaders see the sense of implementation. Trusting the strategy and correctly investing in it is key to succeeding in better data maturity. 

Getting out of the rut is about building a foundation, not overnight success. There are no cheat codes or secret paths to success, but the above methods can set an organisation on the right path. 

This article is part of our Data-Driven SMB series. For more information, advice and resources on how to accelerate your organisation’s data and analytics maturity, click here or contact us today.

Reference

1 Four Steps to Improve Data and Analytics Capabilities When Business Intelligence Maturity Is Low. Gartner, 2018

Business challenge:

Data and Analytics Roadmap
Data and Analytics Strategy
Data Maturity
Tim Archer
Written by Tim Archer Director of Analytics

Tim lives, sleeps and breathes analytics. A Chartered Management Accountant by trade, Tim has been with TrueCue for over a decade, having previously enjoyed roles as a Finance Analyst at Walt Disney and Auto Trader.

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