4 Stages

Stage 01: Data is Accessible

Modern organisations, even in normal operations, generate vast amounts of data. If you run a manufacturing unit, for example, you might already be recording operational and shop floor data extensively.

The data could be captured in files, or electronic folders and data sheets. The challenge at this stage is often that data is captured differently by different stakeholders, functions and locations. Let us say your regional sales teams record the sales data in excel sheets of varied formats. One team captures the date of proposal submission, while the others do not. Also, the format of data is different. Some capture customer names as , others capture it as .

A typical stage 01 scenario: In a large cement & steel conglomerate, air travel data was being maintained in spreadsheets, but in different formats and at different locations.  It took significant efforts to consolidate the data into a common format. Upon applying simple filters on the consolidated data, it was discovered that there were 30% cancellations across the board, causing losses of INR 75 lakhs ($115,000) to the organisation. 

At this stage, the data is available only as individual data points, e.g. PDF scans. To get usable insights, data from all individual files have to be mined separately. For example, say the sales team has the record of invoices sent to hundreds of customers, but only as scanned copies in separate customer files. Creating a quarterly report will require taking information from all individual files.

To mature to the next level, data across sources and locations have to captured uniformly in a transaction based system.

Stage 02: Data Moves Through the Organisation

Stage 02 is where your organisation captures data in a transactional system, i.e. a field based tool. You capture data points and formats uniformly across different locations of your organisation and across the lifecycle of a process. At this stage, we can use different queries to generate primary insights from the data.

For example, you record candidate details across the stages of interview, selection, offer and joining. We can use queries to find out the average turn-around-time from candidate interview to final offer.

A typical stage 02 scenario: A large logistics company based in the Middle East had a transaction based system to capture procurement data uniformly across locations. However, the procurement was controlled locally. Once the data logged in the system started getting used to generate insights, giving local and global visibility on all procurement activities, the organisational profits could be overhauled.

At this stage, you can generate insights about individual parameters and functions. However, analysing multi-variable quantities or the performance of one area in relation to the other, is not possible. For example, you can analyse the performance of the quality function. However, analysing the quality function in relation to variables of the procurement function may not be possible, as those require a higher level of analytics.

To mature to the next level, advanced statistical tools will need to deployed.

Stage 03: Data Analysed for Informed Decisions

Stage 03 is focussed on using mathematical and scientific expertise to unlock patterns and relationships hidden within data. We can aggregate previously isolated data sets and analyse them to reveal unexpected insights.

Your managerial instincts are complemented by deep insights that enable more informed decision-making. Sophisticated statistics, pattern spotting, indexing of data from multiple sources and machine learning algorithms combine to discover previously unknown correlations.

A typical stage 03 scenario: A manufacturing company was looking to bring down the number of defects arising out of varying combination of issues across the production process flow. They had some level of analytics available by running queries, which helped them identify points of defect on the shop floor. However, those insights were not adequate to predict what combination of variables would lead to defects. Upon deploying advanced analytical, the occurrence of defects could be pre-empted and prevented.

At this stage, while in-depth analytics enable sharper decision making, operational approvals still require your sign-off. The nature of these approvals is such that they do not involve complex decision making. Yet, the number of sign-offs are high, as they pertain to regular transactions, consuming your bandwidth, significantly and unnecessarily.

To mature to the next stage, the system is equipped to take routine decisions, flagging only the exceptions that need to be evaluated by you.

Stage 04: Data Used for Automated Decision Making

Stage 04 is when your organisation has reached a level of high quality data management. Here the system can assist you in regular operational decisions, and flag the exceptions for your review and approval.

This enables you and your team of managers to focus solely on decisions of higher criticality. It also creates space to implement improvements, prepare for exigencies and focus on aspects that have the biggest impact on your business outcomes.

A typical stage 04 scenario: A pan-India transport company had developed parameters for most efficient routes and driver allocation, with their advanced analytics. However, the allocations had to be reviewed and approved by hub managers. This amounted to hundreds of approvals every week. However, most decisions required a sign-off purely for the reason of introducing a level of check, if all the parameters had been met. It did not involve decisions requiring the skills of the hub manager. 

The company decided to use the available insights to develop algorithms by which the system could check these parameters, and auto-approve, flagging only the exceptions. This allowed the hub managers greater bandwidth to implement efficiency projects, evaluate new routes, etc, leading to greater profitability for the organisation. 

This is the highest level of data maturity, where leaders can focus on the most critical business areas that require their attention the most.