A task easier said than done.
“Smashing silos” is often presented as a critical step for creating exceptional customer experience. Once a marketing team has access to customer data it follows that they will be better able to create personalized, contextual experiences across a customer journey.
The task, however, is easier said than done.
Ask an IT professional about smashing silos, and you will likely solicit less enthusiasm than people on the business side calling for silo destruction. Those with an understanding of the practical requirements of data access and integration are forgivably less optimistic about the speed of change and the resources required to do it.
The lack of consensus is a reflection of the disconnect between business and IT, according to Eric Thoo, an Australia-based Gartner analyst, who argues an enterprise-level data strategy can help considerably.
“Business think it’s IT’s job to have a vision for data. IT thinks it’s business’s job to have a vision for data,” Eric told Which-50.
“And at the end, the whole responsibility fell into a ‘no-person land’. At best it falls into the lap of a data administrator.”
That’s problematic, Eric says, because data administrators don’t tend to connect data policies to the strategic vision of the organization, nor are they supposed to.
As Eric argues, data is increasingly being viewed as a strategic asset and a fundamental part of enterprise strategy. Gartner predicts that by 2021 the value of an organization’s data will warrant internal information valuation and auditing practices. Early movers on data valuation and leadership are already outperforming the market. Put simply, the value and importance of data strategy to an organization means it should exceed the remit of an administrator.
Indeed, any high-level conversations regarding the strategic use of data now warrant involvement, or at the very least understanding, from executive leadership.
Data integration, Eric argues, is only a part of an overall enterprise strategy around data, but it too requires a collaborative approach from departments.
“It’s not just an IT issue, business of course shares that burden too,” he said.
Often that means a paradigm shift to accompany new technology and its application, one that is consistent with an overall data strategy.
IT and business need to take a more collaborative approach in both procurement and application, while adopting a datacentric mindset in order to fully leverage the technology by going beyond automation, according to Eric.
“Otherwise it’s not a complete picture. I can automate everything, I can serve my customers. What happens to the data? Well, I assume somebody else knows. That somebody else doesn’t exist.”
Unfortunately, this holistic approach to data integration is not common, Eric says. When he asks organizations if they view data as an asset, most agree — but many are still failing to treat it as such.
“Your data is not just bits and bytes sitting in a hard disk in a cupboard or in files. Your data is actually the asset of the company,” he said. “You won’t turn data into an asset just because you kept [it] in a database, just because you put in some data quality tools.”
Once organizations recognize the potential of data and treat it accordingly, its value will be unlocked. However, Eric says, that change can only occur through a more holistic, collaborative approach — one where data is not the sole responsibility of IT.
Build data roads
From IT’s perspective there is data everywhere, and changing applications or where the data is stored is often not an option. Getting to the point where data is integrated across several systems and accessible to multiple stakeholders means “data roads” must be built, Eric said. “What’s left to be done is to build roads to these different premises where the data is.”
“That construction of freeway, construction of bridges is what data integration is about.”
But while creating data roads, it is also critical to ensure data quality, according to the Gartner analyst.
“While moving the data and creating a good traffic flow is good, it’s like delivering a courier package to someone’s home on time. You also want the package that you open to contain the right thing. So, data quality is the other side of the coin.”
So, while access and smashing silos may be the goal, there is an equally important consideration of data quality for IT, Eric said.
There are, of course, other considerations for IT, but these factors are “taking up the lion share of the pain points in a lot of the companies right now.”
The market is responding. Data integration applications are “growing phenomenally,” according to Eric.
“These integration technologies, they don’t help you build application, they don’t automate the business process for you or give you analytics. But what it really gives you is the ‘glue.’”
However, becoming enamored with integration applications presents the risk of organizations repeating the mistakes which created data silos and the subsequent need for integration solutions.
Culture is key
There is no universal approach to data. Its collection, integration, and application will all be dependent on the organization’s use case, existing systems, and budget.
For marketers, for example, off-the-shelf personalization products offer an instant solution much more accessible than customized data systems.
However, the trade-off is often inflexible applications and rigid algorithms unable to be adapted to organizational or even market specific nuances.
The ability to ingest data must also be considered. Some solutions are unable to ingest first-party data, and vice versa with third-party data.
Eric argues the data strategy and culture to support it are paramount, rather than the technology. “Technology will come. We are not short of technologies out there today. What we are short of is this ability to think flexible modes of working with people, process, and technology.”
Eric’s point is a salient one. Success will not necessarily follow new technology. In fact, adding technology has the potential to worsen the silo problem.
As the strategic importance of data becomes more and more apparent, sustainable improvements will rely on organizations developing a data-centric culture — where the lines between business and IT are blurrier than ever, and data systems are just one of many collaborative efforts.