By Tim Lang, Chief Technology Officer at MicroStrategy
In today’s global marketplace, business intelligence (BI) is not just for senior management. Employees from all levels of organisations and across various departments are using BI to drive decisions. Companies now face new challenges in data governance, with this influx of users needing to access and interact with data. Chief Data Officers (CDOs) set strategies for governance programmes, related employee trainings, and bridge the gap between IT and business units. Ultimately, given the greater demand to manage data for new users, the CDO works to minimize the risk of contaminated data leaking into business reports.
Reconciling IT and business with clean, actionable data
Oftentimes, the CDO position is overlooked or undervalued when businesses plan to increase BI initiatives. While this is a common mistake, it can be a costly one as well. If an organisation’s data becomes corrupted, then it is of no value – data must be trusted and verified to be useful. Each team or department at an organisation has its own needs for using this data, and traditionally the IT department focused on managing data. However, as more business users interact with data on their own, and as self-service options continue to grow, the likelihood of data corruption increases significantly with each added user into an environment.
Contaminated data – what’s the risk?
Democratising data has its risks, as minor inconsistencies introduced into data sets can have exponential effects across multiple departments. Inconsistencies rapidly multiply as users unknowingly introduce unverified information with colleagues, clients, and others outside the organisation. Complications originate from issues with ownership, data collection processes, or technology standardisation.
An employee who corrupts company data often does so with no intent, but rather because of a lack of technical knowledge or training. Without even knowing they’ve caused an issue, the data they’ve contaminated can lead to excessive consumption of company resources, increased maintenance costs, and distorted results that end with painful decisions.
‘Reverse engineering’ irrelevant, out-of-date, or erroneous data is a tedious, time-consuming process. It provides an opportunity for the competition to jump ahead because your company resources are diverted to cleaning and restoring data to its pre-contaminated state. To avoid the many pitfalls associated with data contamination, here are five tips to help organisations get data quality and data governance right.
Tip 1 – Implement an established data governance framework
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. Building this foundation of trust is essential for any organisation that looks to obtain precise insight and business value from data assets. But implementing a data governance framework isn’t easy and there isn’t a one-size-fits-all approach. It must be customised according to each organisation to effectively allow collaboration between business and IT departments.
Tip 2 – Identify and appoint key team stakeholders
New roles are being created at varying levels within organisations to effectively manage a governance framework,. These data stewards are critical in curating data and fostering communication between teams. To communicate effectively, each business unit should designate representatives who engage in routine cross-team dialogue aimed at keeping everyone in the organisation on the same page. It is the stakeholders’ responsibility to ensure that their team adheres to established processes.
Tip 3 – Establish direct communication between IT and business
The primary factor in ensuring successful adoption of data-driven initiatives is the partnership between business and IT according to the New Vantage Partners 2016 Big Data Executive Survey. Open communication and collaboration works to ensure that everyone’s data needs are met. Transparency from both sides is key, and while the analytics platform may be able to provide monitoring capabilities to help bridge that gap, the technology itself can only take you so far. To be more effective, governance processes need to be fluid and open, and IT needs to continuously monitor and prioritise how they promote essential measuring tools into a governed framework.
Tip 4 – Make sure the most effective data management technology is in place
The easiest part of the process is choosing the right technology and putting it into the hands of both business and IT users. Technology should enable business teams to control the ‘who, what, where, when, and why’ of data entry – allowing access to information that pertains to them. It should also enable collaboration across the organisation to break down existing data silos.
Tip 5 – Start with small steps
Data governance cannot be created or fixed overnight. With business needs frequently changing, it takes a continual process of identifying gaps, prioritising applications, and promoting assets, so it’s good to start with small steps. As with any initiative, getting buy-in from key personnel is a crucial first step. The entire organisation needs to recognise the value of having an enterprise-wide data governance initiative—whether it’s through standardised technology, systematic reviews, or appointing data stewards.
The next step is to identify and start with an organisation’s most important application. Try to certify or promote a single application each month, and by year’s end, you’re your organisation will have data governance covering twelve critical applications.
As BI adoption becomes more pervasive, and employees increasingly access and interact with data, an effective data governance programme is more critical than ever before. It ensures that a single version of the truth is maintained across all departments of an organisation – protecting the data’s integrity and credibility.
Investing in employee education is a key element to consider when deploying data governance. Preventative measures and early investments work to minimise the likelihood of eventual mistakes or oversights that could damage credibility. Once the right technology is in place and being used correctly, applying a data governance framework will keep a business poised to reap the benefits and gain a competitive edge in today’s climate of continual change and disruption.