Last week we published the first part of our article on omnichannel, which came as a result out of an interview with Duncan Avis, Customer Enterprise Lead at Global KPMG Connected. We said that according to one of the latest KPMG studies, companies successful at omnichannel are also successful at eight core capabilities which imply connecting the front, middle and back offices (see the image below).
In this second part of the article, we would like to touch upon the technical side of their success. In other words, what are the technical prerequisites for enabling these eight capabilities?
To be omnichannel, companies require an enterprise-wide grip on their data
It’s not that companies tend to lack certain IT systems; at least, usually, they don’t. Quite on the contrary, one of the questions at the KPMG study was specifically around the technology in place. All sorts of systems have been named: marketing analytics, marketing automation, in-store and sales solutions, contract and order management, billing solutions, even workforce management and product innovation management solutions.
But what Duncan and his colleagues noticed was that the companies that said they do get their return on investment – in other words, are successful in their omnichannel efforts -, have enterprise-wide data management and enterprise-wide analytic capabilities in technology.
Data-centricity leading to consumer-centricity are two key factors to being an omnichannel business, or as Duncan calls is, ‘omnibusiness’ as opposed to just omnichannel. That being said, one needs to keep in mind that the data-centric approach presupposes agile data integration and smart data analysis.Data-centricity & consumer-centricity are two key factors to being an omnichannel businessClick To Tweet
Yet the current state of data is alarming
How important the data analytics tools are going to be for businesses show the answers of the KPMG interviewees. While data and analytics were named as the third important area of investments currently and within the next 12 months, “it was the one area that had the largest increase going forward,” says Duncan, explaining these insights as: “I think organizations recognize that data and analytics are important today, but they also recognize that it is going to be super important going forward, and it’s no longer about just analytics.”SmartData & DataAnalytics will have the largest increase of investments in the next few yearsClick To Tweet
Data integration tools are often mentioned almost in passing in this regard, yet they do play an important role in collecting data from various sources in the front, middle and back offices, and feeding it to the data analytics systems. Data integration solves the rather classic problem of having disconnected, channel-specific systems in place, which do a good job in their own closed environment, but fail completely when used together with another channel.
Let me give you an example to prove my point. Duncan shared it with me when talking about integrating the front, middle and back offices, but I think it fits perfectly into the data integration topic as well. Many times, because they were delivered by channel, product information management tools used for online have the schemes that are different to that of the core ERP systems used in stores.
Now, imagine you buy something online and you want to return it back to a store. Because of this difference in schemes, there is a high chance that the store’s technology won’t recognize the item even if it looks like their product and, therefore, won’t allow you to give it back this way.
Another recent study named “Data 2020: State of Big Data”, for which a certain market research firm at the commission of SAP surveyed over 500 IT decision makers from enterprise-level companies in the US, Brazil, UK, Canada, Germany, France, Japan, China and Australia, only proves the point. 64 percent of the interviewees admitted low or no accessibility of data to a wide variety of business stakeholders; 85 percent revealed that they struggle with data from a variety of locations, while 72 percent called their data landscape complex, with the variety and number of data sources.
It is safe to say that in the light of the topic of how important data-centricity is, these numbers look quite alarming.
Enable data-centricity with proper data integration tools
In order to ensure proper access to various data sources and locations as well as a clear data landscape, it is certainly wise to bet from the start on smart IT systems, which either provide easy connectivity options or simply cover “it all”.
However, let’s be honest – many established companies that want to go omnichannel now, have accumulated their systems for years, building a complex architecture upon and around it, adding new “layers”, e.g. for new channels, only when needed. There is a very low chance that they will be willing to just scrap all this architecture and replace it with “shiny” new, interconnected systems. That would be exactly what many anti-omnichannel experts warn about: an insane act of throwing out of the window corporate money.
Is there a way around it? According to one of the latest Gartner’s papers “Survey Analysis: Integration Platform as a Service Turns Strategic“, iPaaS is currently increasingly used not only for application and data integration, but also in data warehousing, mobile app integration, B2B integration, analytics and other more or less similar scenarios anyways.
So, modern iPaaS tools might be indeed a way to connect disparate data sources and locations without having to break down the architecture. They are scalable and flexible when it comes to data traffic spikes; they are often hybrid, connecting public and private clouds to on-premises, and as a rule, they are easy-to-use to be a perfect alternative to the “quick and dirty” point-to-point integration. The latter is particularly important unless you want your whole architecture to get eventually so stiff that it will become practically impossible to introduce new systems or remove old one without breaking the whole organization.
Without proper data integration, when you have a grip on literally all data in your organization, accurate data analytics is virtually impossible, which in its turn, impedes data-centricity.
That is why data integration tools should not be regarded as an add-on to connect an application here and there, but one rather needs to approach this questions from a strategic perspective.
If you enjoyed this article, you might also like our other article on four business-critical issues in retail and how they can be solved with smart data integration.