Solve the right problem
Every day, Retail divisions use data for planning, reporting and analysis. While they manage their own business functions, to truly perform, retailers need common data across divisions.
Data is more useful when shared. This was why massive Data Warehousing projects tried to bring data like POS, GL, Web, HR, and inventory into one place.
A problem with these projects was that they were technical, rather than business-driven.
They focused on getting data into the warehouse.
Getting data out, in a useful format, was an afterthought. The warehouse was seen as an end in itself – rather than a means to an end.
This often meant that despite wonderfully automated and cleansed data structures in tables, users either accessed restricted standardized reports that were slow to adapt and needed IT experts, or simply exported data into Excel to support their own departmental needs.
While data warehousing delivered standardization and centralization, it treated business users as unskilled data consumers and did not necessarily support how business users actually got things done.
Gartner, a global research firm, call this Bimodal IT. These approaches are modality One (structured, planned, compliant) and modality Two (agile, emergent, innovative).
Retailers need well-structured enterprise-wide information management. They also need agility and bottom-up innovation from creative and informal business champions who make things happen. And they need to foster collaboration and innovation.
Go beyond discovery
As a reaction to monolithic BI and Data Warehousing, more agile data discovery platforms flourished. While solving part of the problem, they introduced their own challenges.
Firstly, they fought Excel. The Excel interface is not the problem, it’s that each spreadsheet becomes its own copy of a database. Securely manage the data, and you solve the problem – and keep the ease of use and familiarity.
Secondly, they only solved part of the puzzle – data discovery is the first step in corporate performance management. Excel isn’t just a visualization tool. It enables writeback and modelling. So you can’t replace Excel with a read-only reporting tool, no matter how nice it looks. Because reporting, analyzing and planning is an iterative process, data discovery did not stop the proliferation of multiple solutions.
The same data ended up in different tools, used by different departments, for sales planning, for dashboards, for consolidation, and close.
GWA is a leading retailer of building fixtures and fittings to households and commercial premises. GWA are a retailer, wholesaler and manufacturer with turnover over $800m and headcount over 1600.
GWA used a data discovery tool that came bundled with their ERP system. While this met some departmental requirements, it wasn’t tailored to their unique needs. For example, it did not enable them to budget and forecast. Finance still needed familiar and flexible Excel for month-end consolidation and close.
GWA adopted Jedox to go “Beyond BI”. Because Jedox unified business processes, departments could collaborate using a common language. With consistent data, they created models for corporate consolidations, enterprise budgeting and forecasting, KPI dashboards, stock management system, sales & inventory analytics, and sales forecasting.
GWA connect corporate strategy to operational execution, improve stock management and forecast accuracy – in one place.
This is Part 5 in a series on retail value-drivers, how companies use them in planning, reporting and analysis and lessons for mid-sized retailers to punch above their weight and better share information.
The gap between how a retailer (tries) to present itself, and how a consumer actually perceives it comes down to alignment. To provide a consistent experience to the customer, you must ensure business departments align with each other, and with your strategy.
While business divisions exist for different reasons, how they report, analyze and plan is very similar. By unifying these activities using and agile platform, departments can collaborate using common value drivers on shared data.