How To Climb The Data Maturity Scale
Data has become a modern-day asset to organisations. Using analytics to take full advantage of data is becoming an increasing priority across every business sector.
Automation,machine learning and AI (Artificial Intelligence) – all based on the collection and analysis of data – are the buzz words stealing the headlines.
These technologies may well be commonplace for banks, insurance companies or tech giants. But what about the rest of us?
Does your organisation’s data strategy still rely on manual spreadsheets?If it does, you are not on your own.Deloitte research shows 96% of executives believe using the right tools to get the most out of data will become more important than ever over the next few years. Yet one in 10 businesses are yet to implement a big data strategy and less than a third regularly conduct big data projects, according to an MHR Analytics survey.
While many are still to embrace data analytics, there’s no denying that the race to adopt this technology is gathering speed. Choosing to advance your data journey is essential for competing as well as for achieving your organisational goals.
Evidence about the advantages of data maturity is widely available. In one example, a survey by MIT and IBM found that organisations with a high level of data maturity had 8% higher sales growth, 24% higher operating income and 58% higher sales per employee.
What are the first steps to progressing your data journey and climbing the data maturity scale? What in fact is data maturity?
“Data maturity is the extent to which an organisation utilises the data they produce,” said data maturity advocate Laura Timms, product strategy manager at MHR Analytics.
“An organisation that uses advanced business intelligence software to analyse its data can be considered far more mature than one that relies on spreadsheets for reporting,” she said.
“Data maturity is equipping data-driven organisations with insights that supercharge their own efforts, allowing them to break free from the limitations that would otherwise hold them back. Many leading companies understand this and are using their data not only to improve their core operations, but to launch entirely new and improved business models Many others however, are struggling to make sense of the hype and the excess of information about aspects of big data, and this is creating barriers to the business case for advancing the data journey.”
Advanced data maturity is creating an uneven playing field in many industries, with data-driven organisations stealing market share in some hyper-competitive markets.
The five stages of data maturity are:
STAGE 1 – Operational. Reporting is limited to tasks that are critical for business operations, with no formal BI (Business Intelligence) and analytics tools or standard in place to support this, and spreadsheets used as a primary means of reporting.
STAGE 2 – Descriptive. BI and analytics are in their early stages of implementation and are used to report on activity.
STAGE 3 – Planning. Using tools like scenario planning, BI and analytics are used not just to report on what’s happening, but to plan for the future.
STAGE 4 – Predictive. Data analytics is used to predict what will happen five, ten, even twenty years from now and to pinpoint the key drivers of trends.
STAGE 5 – Prescriptive. Users no longer have to input variables into the system to predict future outcomes. Instead, Machine Learning and AI make it possible to detect issues before they’re even considered.
A free tool providing downloadable data maturity guidance has been launched by MHR Analytics.
The MHR Analytics Data Maturity Quiz assesses where businesses are on the data journey and produces tailored, practical steps to progressing higher up the scale.
The tool also aims to demystify some of the jargon about data and provide a non-nonsense diagnosis.