Reducing Friction with DataOps
- Details
- Written by Paul Lewis
Businesses are intolerant of inefficiency of any kind, especially if it takes the form of people, process, or technological bottlenecks. Businesses expect their day-to-day operations, their hierarchies and organizational structures, their supply chains, and their IT operations to run as smoothly and efficiently as possible… to be frictionless.
On the other hand, we can easily imagine scenarios in which friction functions as a kind of necessary, essential, and – in the proper context – advantageous constraint.
At a basic level, friction is what makes Usain Bolt go. It’s what enables Simone Biles to stick her spectacular landings. Most of us can easily imagine how friction operates as a positive factor in the design of mechanical parts too: tires, brakes, transmissions, flywheels, and belt-driven pulleys all depend on friction to work.
Like it or not, friction fulfills an important function in any system.
Friction in data management and data governance
This is a good segue to the crux of this post. The points I want to make are two-fold. First, friction is actually a useful concept in both disciplines. Governance, for example, is the inevitable product of friction between two or more opposing forces or entities – divergent values, priorities, purposes – that come into conflict with one another. In a sense, governance is friction. Albeit friction of a necessary and essential type.
Second, friction is a positive force because it makes certain things hard to change. If this point seems counter-intuitive, then think about your high-value investment in your management information systems (MIS) infrastructure. Companies pour millions of dollars into designing and maintaining their MIS infrastructures. As much as companies like to complain about the state of their data mart, data cube and ETL assets, people depend on operational reports, dashboards, KPIs, and scorecards to support day-to-day business decision making. As a result, IT doesn’t introduce new data sources without thoroughly testing and trouble-shooting them. This is one reason it takes so long to provision new data or analytics in a conventional data warehouse architecture. Another reason is that, until recently, IT didn’t have any other options. Now it does. I’ll say more about this below.
Self-service is a response to friction
If it seems that IT’s priorities are at odds with the self-service ethic, it’s because they are. This is less of a problem than most of us realize. If you think about it, we’re used to framing the relationship between data management/data governance and self-service as antagonistic: a zero-sum collision of top-down authority with bottom-up insurgency. Thanks to technological and economic disruption, it’s now possible to see it in terms of both, not a zero-sum relationship.
First, some obligatory background. Self-service tools were a response to users’ frustration with unnecessary forms of IT friction, such as overly restrictive data management and data governance controls. Self-service forced IT to reassess what was necessary in both data management and data governance. Consistent, conformed data is less necessary for self-service, analytic discovery, data science, data mining, and machine learning. On the contrary, access to the data is essential for these and similar disciplines.
Fast-forward to the present… DataOps
IT is under pressure to incorporate data from more and varied sources into the operational reports, dashboards, and scorecards that power critical day-to-day business decision making. There’s pressure to enhance these MIS assets with new KPIs and new analytics. There’s pressure to use these assets to accommodate new practices, such as self-service discovery, data science, and machine learning, as part of new development paradigms for which they’re fundamentally ill-suited.
That’s the crux of the problem. IT is under pressure to accommodate an entirely different data management paradigm: DataOps. Traditional data management presupposed a centralized site for data access and data processing. This was the data warehouse. In the DataOps paradigm, the data warehouse is one among many data sources and data processing engines. Data isn’t just created in a diversity of contexts, it is accessed, moved, and processed in a diversity of contexts too.
DataOps assumes that for data access and data processing:
- Data may originate at the enterprise edge and be processed before being vectored to its many and varied destinations.
- Data engineers and data scientists will build multi-stage pipelines which vector data to and from multiple data sources and/or processing engines.
- Data will stream continuously from IoT and telemetry signalers, on- and off-premises RESTful services, and the like.
Retrofitting an MIS infrastructure for DataOps involves introducing radical changes into well-understood and strictly governed data integration processes. It also expects data type diversity beyond just OLTP systems. Unlike MIS, DataOps presupposes a federated model. You can’t square this circle by retrofitting it.
Squaring the circle
So how do you square this circle? How do you accommodate a set of demands and priorities that seem to be at cross purposes with one another? The solution as I see it is actually pretty simple: you cut the Gordian Knot. You don’t even try to bend your existing MIS infrastructure to support fundamentally new DataOps-like practices and use cases.
The sensible thing to do is to draw a square around your circle – i.e., to create an environment just for DataOps. You can exploit disruptive technologies, like public and private cloud services; converged infrastructure; and distributed object storage, to build a parallel DataOps environment that supports self-service and similar use cases. Even better, your parallel environment would consume exactly the same data that flows into your governed MIS systems – extracted from the same OLTP sources and applying the same transformations. But it won’t impact, or, above all, change your MIS infrastructure.
Best of all, you can slipstream new sources of data and new types of analytics from the parallel DataOps environment into your bread-and-butter MIS infrastructure once they’re proven to be useful and mature. Call it whatever you like: a data lake, a data hub, or a data refinery; the important thing is that your DataOps environment is ideally suited for self-service discovery, data science, machine learning (ML), and artificial intelligence (AI). Like it or not, DataOps requires a fundamentally different kind of friction – i.e. different constraints and controls with respect to how data is sourced, how rapidly it is provisioned, how it is engineered and/or changed, how it is used – than core MIS.
Making friction your friend
To sum up, there’s essential friction in both environments. It’s just different kinds of friction. Some controls (i.e. controls that govern the use of personally identifiable information - PII) will be consistent across both environments. Even so, the parallel DataOps environment might make use of automated mechanisms, such as data masking/hashing or differential privacy algorithms, to enable data scientists and ML engineers to use data that contains PII without exposing the PII in a way that is inconsistent with governance goals or controls. In addition, some contractual and regulatory requirements will also be consistent across both environments. The parallel DataOps environment lets organizations diversify the insights they produce, as well as carefully and deliberately incorporate this diversity into their core MIS processes. In this way, the organization can introduce predictive and prescriptive analytics into its fragile (and tightly controlled) MIS infrastructure.
We’re transitioning to a model in which we manage, secure, and control data in a federated way. At Hitachi Vantara, we like to call this “Edge to Outcomes.” Let friction and the feedback it generates help you navigate this shift. Let friction help you determine the pace at which you shift your MIS systems, processes, and workloads to other contexts – be it the on-premises private cloud, the virtual private cloud, the public cloud or, conversely, get enriched with data or analytics that originate in the cloud. Let friction help you determine the pace at which you use IoT data from the edge to enrich operational reports, KPIs, dashboards, and other business-critical MIS assets. Let friction help you determine the pace at which you make data and analytics available to or from external sources. Let friction help you determine which data you need to keep in place where it can be processed in situ. Let friction be your friend, not your foe.
Paul Lewis is Global Vice President Industry and Enterprise Architecture of Hitachi Vantara and an advisory board member with The IT Media Group.
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