There has been great discussion lately on analytics and automation and its ability to bring value to businesses. Despite all the hype around it and its perceived benefits, particularly in boardroom discussions, many executives continue to feel that extolling its importance has become a fad.
The reason for such dissonance is because deliverable value to the end user or consumer is often intangible and, as a result, invisible. In many ways, practitioners of analytics operate like consultants. Their task involves storytelling as one of their key responsibilities.
Data—leave alone analytics—means different things to different people. Transforming data into information and actionable insights involves a creative approach. Organisations can accrue great benefits by interpreting data and applying the derived predictions in real-life scenarios.
The progress of technology today allows a variety of approaches to enable analytics delivery to be automated processes. We must, of course, focus on process automation and elimination of manual tasks. Analytics should thus be designed into triggering processes or task execution, which automatically brings it within the scope of automation. By itself, analytics offers insight and does nothing more than provide information. But its value is best realised when this insight is embedded in business operations, making operations intelligent, proactive and seamless.
Identifying areas of implementation
In operationalising and embedding analytics into design thinking, we need to identify the areas where data is available, offering optimisation opportunities. We need to ask the right business questions that enable future scenarios:
• Where were we?
• Where are we now?
• Where do we want to be?
While these questions sound straight out of a strategy development design document, they answer the direction you would like your line of business (LOB) to take. A more granular action plan will contain information about soft spots, where interventions will bring new value across business processes.
Realising the importance of analytics
Often, in applying analytics, you need to address specialised teams. Are they in a position to understand the value of the data they own, generate and process? How well do they grasp the differences of interpretation and prediction? Do the key performance indicators (KPIs) they work with have an ability to blend analytics into them?
For example, finance departments typically ‘live their lives’ in data. They also have a surfeit of systems that churn out information: Statutory, regulatory, and governance requirements.
This data, however, is greatly enriched when overlaid with enterprise data. This can be enabled only by applying processes and systems intelligently into the department’s activities. For example, a ‘Budget versus Actual’ report predicts which line items are likely to miss quarterly/annual budgets based on underlying key drivers. (This report is system generated and not orchestrated by someone in the finance or corporate department).
The automation paradigm
Analytics deployments are greatly reinforced when accompanied by automation, making them more efficient and effective. This is also non-invasive to the ‘as-is’ business process that runs ‘on the floor’. Users are not pushed to recreate their operating environments. The route to embedding analytics into the business involves identification of processes that are either repetitive or require heavy lifting across multiple tasks (read process handoffs), which results in frequent errors.
Sometimes, even with the deployment of automation, businesses fail to recognise its value as the stakeholders take the tool for granted. In back-office transaction processing, automation has been around for a long time. It is common for processes to change with the business. In most cases, the existing transaction platforms come up for upgrades within two to three years.
Given the time cycles of IT spends and upgrades, these take much longer and quick fixes are required to manage additional manual efforts. Process reworks and workarounds become necessary to manage BAU. To unlock value and realise intelligence from automation, we can today use machine learning technologies.
Machine learning (supervised or unsupervised) can be enabled to apply intelligence to the handling of income transactions. By segmenting the source of the transaction and other attributes, systems can be made to expect errors and treat transactions accordingly. With systems thus supporting workers or agents to be more efficient ‘on-the-fly’, they can work with fewer errors, or provide services that improve customer experience.
Building the future framework
Bringing intelligence to automation should be seen as an ongoing activity in managing the process. The market is coming up with tools that are designed specially to evaluate customer sentiments as well as to provide metrics for translating into better user experience.
There should not be any hierarchy when it comes to looking out for opportunities for smart tools. Executives who manage the daily transaction volumes need to be trained to look out for such opportunities. The automation process should encourage easy adoption and seamless integration into the operating systems.
The adoption of new technology does not need a capex budget. Open source tools can be smartly used to create an ensemble of algorithms/systems to support the desired state. Vendors are ever willing to work on an agile plan, and these can run parallel to the main system, until a decision to merge is planned.
The shift towards analytics and automation should be seen as a natural evolution of business processes. A smarter workforce works with smarter tools. This in turn enables them to handle larger volumes and deliver better results. Also, market and customer segments are changing, and companies need to focus their efforts on managing their expectations through such intelligent automation.
- By Shankar Subra M., Director — Analytics, Concentrix
The thoughts and opinions shared here are of the author.
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