Data-driven organizations know how to turn data into business outcomes — the first step in this process is parsing actionable data from inactionable data.
Businesses today recognize the importance of the ‘big-picture’ view. Informed decision-making demands context — and lots of it. But most decisions happen at the edge of operations, where too much context (i.e., irrelevant data) can, in fact, slow the decision-making process. The task then is to ensure that decisions are being made with just enough context, and not an iota more than necessary. This directive falls to CIOs, who must design, implement, and manage information systems that give decision-makers this ‘Goldilocks’ perspective — rather than an overcrowded big-picture overview. In short, they must parse the actionable data from the inactionable data, and make sure it gets to the right place at the right time.
What makes data actionable? Beyond being relevant, actionable data can be shared, used, and viewed as needed to allow for successful decision-making. When data silos exist — whether due to cultural habits or outdated IT infrastructure — appropriate contextualization becomes nearly impossible. To look ahead, CIOs must focus on integration — combining data streams, making data available across departments, and building solutions that streamline the process of turning data into intelligence, and intelligence into action.
Why Integration Matters
In today’s day and age, data is integral to the operational and strategic arms of just about every enterprise. But that doesn’t mean every business is working with an optimal IT architecture. Data silos continue to hamper many company’s efforts to leverage their data assets.
Part of the problem is that it’s all too easy for data to remain siloed. IT teams may configure legacy systems and real-time data streams so that they’re only accessible for IT personnel. Or data may end up siloed at a departmental level, with decision-makers unable to see the information that could help inform their actions. Some of the consequences of this are obvious. Managing a multitude of dissimilar data streams is naturally more challenging than dealing with traditional IT architecture. What’s more, siloed data can end up wasting resources if departments have to re-create and duplicate data sets, or if IT has to later construct manual workarounds to deliver that data.
More importantly, however, the effect of this siloed data can impact overall company effectiveness. Data needs to be delivered across the enterprise if it’s going to benefit the business as a whole. Operations, sales, marketing, finance — these teams shouldn’t work in isolation when their decisions impact every part of the business.
Businesses are beginning to take steps to break down data silos as they digitally transform, driven on by experts who argue that the “360-degree view” is the best way to put data to use. But what businesses may misunderstand is that a 360-degree view doesn’t necessarily mean surfacing all possible data — in fact, it shouldn’t. A 360-degree view should provide the right contextual information across all teams, but it shouldn’t mean inundating them with irrelevant data. Integration is crucial to providing all teams with the same foundation, but key, focused information within that foundation is what moves organizations forward.
How to Ensure Your Data Benefits the Whole Enterprise
As you build out your data landscape, you should focus on technical integration along each step of the process. To start, that means choosing software that “plays well with others.” Certain proprietary, standalone applications can make integration difficult or impossible. Heterogeneous databases aren’t built to integrate automatically, however you can conscientiously choose systems that allow data to be easily pulled out and manipulated. Of course, in many cases, you’ll still end up with a range of applications and some legacy systems that will incur a bit of extra work.
Data integration has long been a problem for CIOs. And when it comes to applications where anything besides real-time data is useless, pushing data to and from the cloud often isn’t an option. In some cases, it’s possible to use structural metadata to standardize and thus integrate data, or you can consider unstructured data hubs or lakes. However, these are merely tools that may or may not have a role to play in what must be a broader strategic approach to the activation of data.
The key is to implement a custom-built application that can pool and organize data from heterogeneous technologies and even legacy systems. This means creating a platform that translates a variety of data sources into a usable, easily readable format. Certain backend features can make this kind of data management much more straightforward. For instance, a document database like MongoDB can simplify the integration of new data without the complex data structuring required of relational databases. GraphQL enables efficient data queries across sources; and horizontal, cloud-based scaling is in all cases critical, given the growth trajectory of global data production.
Integrate & Activate — The Enterprise Data Commandments
Far too many enterprises have misunderstood the directive, “break down your data silos,” to mean that data should merely all be brought together under the same proverbial roof. In the first place, true integration demands a whole lot more. Enterprise data need not only occupy the same digital space — it must also be structured in such a way so as to make it functional for, and accessible by, the applications that need it. And perhaps most importantly, it must be managed with as great a degree of efficiency as is required to make it accessible in the very (often fleeting) moment in which it has any use at all. The actionability of data is highly sensitive to time — to ‘catch’ data at its most actionable, information systems must be designed for speed, efficiency, and with the utmost care.
In the second place, simply making data broadly available throughout an organization doesn’t necessarily produce positive outcomes — as, for example, when it leads to data overload, alert fatigue, or “crap on a map.” For a multitude of reasons, designing systems capable of intelligently distinguishing between actionable and inactionable data poses a significant challenge. It’s an ongoing process, and one that’s occurring at extremely high volumes and incredible speeds. This is where it helps to have a powerful suite of cloud services built to enable the development of custom apps with data integration and activation in mind.
