• Vincent Lancaster – Guest Writer

Data Analytics Is Alive More Than Ever!

Updated: Dec 19, 2020

Get With It or Get Left Behind.

First, let us understand what data analytics and why it’s important for your business. Data analytics is the process of collecting and organizing data to draw helpful conclusions from it. Data analytics plays a key role in transforming business operations, creating new business models, and unleashing process improvements. For a long time, analytics has held out the promise of helping businesses to gain true insights from their data. But the need for it has never been more pressing than now, at a time where many of our past assumptions are no longer true. That’s because even as normality of sorts is slowly returning, life after the pandemic is fundamentally different than before in many ways. Old data that predates Covid-19 will tell a business little about how customer habits or spending patterns have changed. A transport company, for example, would need to know about the number of journeys people are now making because despite some easing of travel restrictions, many businesses are asking their people to work from home where possible. Passenger data such as ticket sales or journey flows from 2019 would not reflect the situation we’re now in or allow modeling of possible future scenarios. To pick another sector, retailers now need to completely reassess all of their plans in light of everything that has changed. Consumer behavior is radically different than before Covid-19, the interaction between physical and digital retail has changed. Brands need to reassess their omnichannel strategies and watch the shift in buying patterns online. In the physical stores, there are new rules around sanitization and signage which are subject to updating and changing. These factors affect just about every aspect of a retailer’s business, from marketing plans and service levels to store opening hours and staff resourcing. Traditionally, companies doing analytics have trained their sights on data within their organization. It’s increasingly clear that the future of analytics will involve a more complex set of data from multiple sources. Often, the source of knowledge lies outside the company, or by combining various sets of data. For example, in a retail environment, changes in commuting patterns may affect the numbers and types of people visiting your store and their reason for visiting – there may be value in incorporating data sets about journey flows into the retail optimization. It’s all too easy to get focused on the data, but as I alluded to earlier, the biggest challenge here is not the analytics: it’s the operational design of a campaign so that the people running it can learn from early results and adapt mid-stream if necessary. This part of the project is a people rather than a technical challenge: to ensure the people and teams involved are flexible enough to be able to respond quickly if a change is needed. This approach calls for linking planning and operations more tightly than ever before. Another reason why I emphasize the people aspect is that analytics is not about fully automating decision making. In our experience, most of the value in this new kind of analytics is in supporting people to make better decisions, not removing them from the process. It’s about helping people to see farther ahead or have a clearer view of the best set of decisions. I use the words ‘set of decisions’ deliberately because too often data analytics is presented as if it gives a single right answer when that’s not the case. No model or simulation can do that: it gives a range of different outcomes to a degree of probability.

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Vincent Lancaster M.B.A. CPA – Guest Writer

Vincent Lancaster is a business manager and analytics expert.

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