Every day, I’m tasked with helping some of the largest organizations in the world unlock the value of their data. Their goal is to interpret large amounts of information and improve decision making in order to cut costs and identify new opportunities. According to the new Worldwide Semiannual Big Data and Analytics Spending Guide from research firm International Data Corporation, worldwide revenues for business analytics will grow to nearly $187 billion by 2019. Impressive, right? But in delving into that prediction, I wonder if organizations are as prepared as they need to be to begin collecting, analyzing, and acting on their data.
As businesses eagerly increase investment into initiatives centered around business intelligence, one task that is often overlooked is identifying someone who is responsible for keeping millions or billions of data points honest, organized, and insightful. After all, what good is that information if it can’t be trusted and verified?
Different teams within an organization—despite sharing a common awareness around just how valuable company data can be—have different priorities, commitments, and agendas when it comes to that data. This is especially true for IT and the lines of business, the two key players in this space. Traditionally, the main business intelligence prerogative for IT was to focus on managing the data behind the reports. Business teams simply called on that information when relevant. However, as access to data became accessible to more employees both within the IT and business divisions and outside of them, it became increasingly possible to manipulate data of any kind.
The Risk of Contaminated Data
Contaminated data is a dangerous realization for an IT or business department. Complications can originate due to information ownership, data collection processes, or technology standardization (or the lack of it). These inconsistencies often rapidly multiply and result in contaminated data where users unknowingly introduce unverified information and, worse, proceed to share it with others.
An employee probably doesn’t have the malicious intent of contaminating your company’s data accuracy, but a lack of technical training could lead to error without even knowing it happened. The ripple effects of such an occurrence can be devastating. Contaminated data can lead to excessive consumption of company resources, increased maintenance costs from a technical standpoint, and distorted results that end with bad and painful decisions. Reverse engineering a problem to sort through irrelevant, out-of-date, or erroneous industry data is tedious, takes up valuable time, and lets the competition get ahead.
The Solution? Implement a Governance Framework
These situations can be avoided with a dedicated and unanimous nod towards data governance. A governance framework sets the parameters for data management and usage, creates guided processes for resolving data issues, and enables businesses to make decisions based on high-quality data and well-managed information. It’s essential and, more often than not, a must-have for any organization that looks to pull precise insight and non-dubious business value from their data assets.
But let’s be clear; implementing a data governance framework isn’t easy and there isn’t a one-size-fits-all approach to how that framework should look. For business and IT departments to find common ground and influence insightful, data-fueled decisions, they must collaborate around a governance framework and lay a foundation for data that the entire organization can trust.
That’s easier said than done, and why I like to approach this partnership through three main disciplines. I call them the “Three P’s” of data governance: product, process, and people. Only when all three are working together can your IT and business teams establish a framework that the entire organization can adopt.
Product: Is the right technology in place?
Putting the right technology in the hands of both business and IT users is possibly the easiest part of this process. Technology should enable business teams to control the who, what, where, when, and why of data entry so different functions within the organization aren’t able to influence information that doesn’t pertain to them.
Are teams able to collaborate within the technology? Does it provide the necessary workflows for IT to easily “promote” business user data mashups to a centralized/certified model? Are you able to quickly and accurately monitor and identify anomalies or determine business impact to help quickly provide teams with the information they need?
Technology plays a crucial role in an overall data governance strategy, and if IT and business teams understand up-front what the technology should enable their teams to do, they can find the solution or solutions that fit their organization best.
However, technology is just one part of the framework.
Process: How will everyone’s data-related needs be met?
According to the NewVantage Partners 2016 Big Data Executive Survey, business and IT partnership was cited as the number-one factor in ensuring successful adoption of data-driven initiatives. Successful organizations have developed a common process on how data is organized, managed, and processed that is built around a set of data governance principles and practices.
Don’t wait to adopt a data governance program after poor data leads to a bad business outcome or after an error occurs that no one takes responsibility for. Have a proactive process in place with clear responsibilities drawn out and the technology to support those needs.
Data ownership is a shared responsibility for business and IT teams, and governance must be managed as a business function like finance or human resources. A collaboratively built process will begin with the priorities specific to each team and clearly defined roles for all involved.
Open and upfront communication is the best way to ensure everyone’s needs are met. Business teams must be transparent about their needs and respect that accurate information takes time, while IT must prioritize turn-around time for business users and keep business aware of the technical commitments necessary to generate this information.
A clear process allows data-driven organizations in any industry to build a bridge between technologists and decision-makers.
People: Are the right people identified?
Employees are the centerpiece of the governance puzzle, and there are new roles being created to help curate data and manage the processes we talked about. The chief data office (CDO) or data steward roles are critical new players whose goal is to curate data and foster communication between teams. In fact, according to Gartner, the CDO position will be filled at 90 percent of large companies within the next three years.
There’s great value in establishing a liaison and mediator between business and IT team leads. This helps business teams work with IT to maintain information protection, governance, and data quality while also working with business representatives to create value from data assets faster.
The governance protocol then moves down the ladder to all aspects of the business where data is involved. Each business unit needs a representative to make sure that their team is up-to-speed on the process for inputting and drawing data and trained with the technology that enables them to do so.
Data governance is not just about technology. It’s about key stakeholders and employees creating processes and best practices to properly organize, validate, and derive business value from their own information. Without the appropriate framework in place to allow the product, process, and people to work together, an organization’s entire data strategy may rest on shaky ground and bear the risk of contaminated information. It’s imperative that business and IT teams work closely together in developing a process, implementing technology to support the process, and identifying the people to manage it all.
Vijay Anand is Senior Director of Product Marketing at MicroStrategy. With over ten years of experience, he has served in several capacities including roles in consulting, technology, and marketing. His main areas of focus are self-service analytics and Software-as-a-Service BI. A graduate of Duke University, Mr. Anand has previously worked with General Electric and TATA Consulting Services and has developed his own start-up business.
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