A Proactive Analytics Modelling Approach for Sustainable Growth and Operations

Data analytics is touted as a business and operations game-changer.

Over the recent years, the art and science of drawing insights and co-relations from data intrigue many. But, how many organisations has actually achieved the intended purpose of leveraging data to advance operations and growth?

It seems that many organisations fall into the silo data limbo. Despite investing much in analytics and its implementation, the storing and leaving of data in silos hinder the intended strategic growth and sustainable progress.

This foreseeable yet pervasive challenge prompts me to pen down on how organisations can work around to solve the root of the problem – to achieve its return of investment in the long run.

Paving the Upsides with Analytics Modelling

The solution to the silo data conundrum could lie in both communicating and developing analytical models.

Proper knowledge management and transfer practices can be put in place, ensuring a seamless flow of data across the organisation. It is vital to involve and communicate with the staff from identified departments on how their data sharing and contribution can make a difference to the organisation.

With all this data and knowledge distilled into an analytical model that also offers forecasting of future trends, staff of all levels will be able to perform to the best of their abilities with less training time.

The science behind these analytical models harnesses artificial intelligence (AI) solutions. Processes are automated, reducing the time required to work on respective tasks. This in turn churns out significant dollar savings for the organisation, as analytical models pave the way for more efficient use of resources.

As we seek the best solution for the largest impact, getting the buy-in from employees is just as important as support from top management. This is because when it comes to embracing new technologies, it often starts from the ground.

To keep up to the technological transformation pace, it spans beyond implementing models and analytics.

But if the return on investment and the perceived benefit of the analytics modelling are assessed to be better than not having one, then let’s go for it.

Adopting the right modelling methodology with design-thinking

Analytics is not an island. We work very closely with customers in the design thinking and enterprise architecture to ensure that analytics will merge seamlessly into the overall business objectives, systems design and implementation.

To keep operations sustainable, we focus on delivering value to the organisation by solving their pain points.

The next step is to decide on the modelling methodology.

Whether to go with “digital twinning” or “a full-fledged simulation model” the decision-making factor is what makes your methodology effective.

We have to consider, why some of these complex and cutting-edge analytics methods are in vogue or why advanced analytics is not always the determining factor in all situations.

Sometimes, a simple rule-based extrapolation can meet the same requirements and achieve the same outcome – or even better.

It is good to spend some time drawing on past experiences and exploring how different techniques have been used on different applications. Compare the outcomes and then build on the best way to deliver the project.

Organsiations can leverage data scientists who are well versed in a wide breadth of different techniques across multiple domains. The most experienced ones know how to ask pertinent questions and elicit responses from customers that can immediately determine if a complex solution is required.

The latest innovative techniques may be highly sought after but it is important to implement the appropriate tools that have unique strengths and serves its own function to meet business objectives.

It would be wise to go back to the drawing board and look back at the problem statement and to evaluate if the best way was deployed to solve the problem. If not, it would be advisable to adopt an iterative approach to re-address the problem statement again.

Every tool has its right purpose – you just have to find the right one for the job.

Embedding analytics into work processes

Over the years and many a times, we have seen projects working well only when organisations incorporate analytical models and facilitating connections to other systems to enable enterprise-wide data science. Some of these models built on the platform include a simple rule-based extrapolation model for contingency simulation, directed graph models for resource optimisation, as well as complex demand forecasting models.

For example, the forecasting model is an ensemble of modelling techniques that account for features such as previous trends, meteorological patterns and time alerts such as holidays and day of the week. But it does not have to be this complex. In the case of a contingency simulation model, a linear progression model with configurable inputs is most likely to be the best fit for the job.

I am heartened to have deployed analytics platform that enables models to be easily integrated with existing systems in the company’s inventory, making complex modelling and analytics transparent to the company’s daily workflow. This achieves our desired goal of analytics embedding seamlessly into the organisation’s business processes.

With the evolving needs of this digital world, it is vital to match the increasing needs of the organisation with the required technology tools to stay relevant and competitive. Analytics systems offer organisations a catalyst for change – to improve work processes, boost bottom lines and even predict what’s next. Achieve sustainable business growth and chart your progress with a clear vision mapped with distinct strategic goals to be realised. Plug into analytics modelling for a better future.