Tackling the data stream is tough for brands in any industry and the retail sector is no exception. Data streams in from across the supply chain, from multiple customer touchpoints, and from digital sources such as social media. Harnessing this massive influx of data is a key challenge for the modern retail sector.
How a retailer manages and makes their data is of increasing importance to their competitiveness in a tough industry.
With retailers facing tighter margins and competition on an international scale, data can really make all the difference to getting ahead.
Retailers are using their data to manage their supply chain more efficiently, reduce return rates, improve store management, design better products, and target customers more effectively.
Data-driven decision making can also lead to smarter retail strategy. In fact, data can assist the retailer to confront many of their biggest obstacles to success.
The silo problem
One of the key challenges for retail is that data gathered across a business often exists in silos. Information from different sources is stored in separate applications, often in ways that aren’t compatible with one another.
In a modern retail environment, data comes in many varieties: structured data from files and databases, and unstructured data such as social media posts. That adds an integration challenge into the mix.
Pulling the data out and feeding into the CRM or analysis system is often a major administrative hurdle. It’s often also raw, dirty and full of duplications and discrepancies.
As a result, data consolidation is a major obstacle to overcome. Data integrations can be tackled either by building custom integration system or using a vendor platform that can accommodate all your types of data.
A company needs to agree to tackle data silos in a united manner because the data silos need to be tackled right across the organization.
It’s important that regional offices and different business functions share a common ambition to address the problem, otherwise, they will continue to hold their own data in silos.
It’s important that more silos don’t arise in the future, so you’ll need to manage an education program that explains why integration of data systems has to be maintained.
Skills shortages
Another of the challenges that retailers are presently facing is a dearth of skilled and experienced workers who can support their data initiatives. In recent years there’s been an expansion in training programmes in data-related fields but it seems that there will still be a shortage of sufficiently trained people for the foreseeable future.
Deloitte quoted IDC data about the shortage and noted that even the expansion of data courses doesn’t mean there are more experienced data workers in the jobs market – which is what industry really needs.
Fresh young graduates may be educated to tackle data issues, but they may not have organisational and project experience in addition to an academic background in this area.
Major retailers may be able to tackle this problem by recruiting straight from campus using internships and other incentives and offering a solid and rewarding career path for graduates.
A few years ago a Bain study suggested that big employers weren’t really offering an environment where data workers can thrive. When companies are competing for rare skills, it’s important to consider what they are offering candidates in terms of career path and working environment.
Some huge companies such as Cisco have chosen to develop their own internal programs to create the data skills they need. Cisco set up its own education program in partnership with two universities, including a data visualisation lab, as a solution to the data skills shortage.
The scale of this program is probably beyond the capabilities of most companies, but smaller organizations need to adjust to the reality that they may need to fulfill their data skill roles by developing their internal capabilities.
Creating data-driven strategy
The final challenge of data management is crafting and implementing a winning commercial strategy off the back of it.
Data can be used to inform how retailers operate, and how they serve their customers, improving efficiencies and creating more targeted marketing. But it can be difficult to interpret data and turn it into commercial strategy. Not all business managers are data-savvy, and there’s a shortage of data manager skills just as there are shortages of data scientists.
Exceptional retailers are ones that are able to use data to adapt in real time and anticipate what to do next using machine learning. Using real-time data, ie monitoring data as it emerges rather than at the end of each retail period, helps businesses spot data trends as they unfold.
If retailers can respond quickly to real-time data it gives them the ability to respond quickly to falling stock levels, perhaps shipping out new stock automatically to areas where it is most in demand. This improves efficiencies because brands can respond to consumer demand more effectively and maximize selling opportunities.
Another way to maximize opportunity is to employ machine learning in an attempt to anticipate the customer’s next move.
At an individual level, a consumer’s historic behavior doesn’t always predict what they’ll do next. Machine learning needs to draw on other sources of information, such as wider retail industry trends and even factors such as the weather, to construct a picture of their likely next move.
Real-time analytics should also support the business in order to respond to a customer’s latest activity.
There’s no easy answer for how to respond effectively to these strategic challenges. The best approach begins with accepting that the organization has to put data at its core, and abandon old structures and decision-making processes that ignore data.
Putting data at the heart of the organization will shake up every function of the business, and this change needs to be approached in a united manner. That’s perhaps the key data challenge businesses face: getting buy-in across the organization and taking a joined up approach.
Confronting this takes decisive and convincing leadership from senior decision makers as the organization orientates itself around the new, data-driven approach.
