Data governance with self-service data analytics – what to look for

Self-service data analytics is great, but it’s not without its hiccups.

A client learnt this the hard way after implementing self-service BI in their IT operations without giving much thought to a governance framework. While the company did make some gains, they have also encountered a lot of problems.

“Data quality has come under serious scrutiny,” the company representative explained. “I can share the specifics with you later, but it’s had a huge effect on our findings. The CEO is also starting to ask questions about compliance,”

While there is no denying that self-service data analytics is incredibly powerful, it is also just as important to recognise that it needs to be regulated with a data governance framework. Without that framework, your business is going to face a lot of problems. Let us explore some of the dangers of neglecting data governance and how to resolve that problem.

Dangers of neglecting data governance

Neglecting data governance hurts businesses on two fronts: Revenue generation and data compliance.

No data governance leads to compliance problems

Data compliance is something all organisations should be concerned with. The world’s governments are starting to wake up to the power of data and passing laws to regulate access.

The GDPR passed by the EU is the most comprehensive law on data access and usage. But there are others to consider as well, especially for companies operating in several parts of the world. For example, Australia has the Privacy Act.

To be clear, I am not trying to suggest that organisations are actively violating data laws without data governance. But, if there are no standards in place, it becomes difficult to verify if organisations are breaking compliance laws or not.

Business-related problems

Besides the problems related to compliance, there is also the issue of business outcomes. Data governance not only helps with compliance but also improves data quality. Without data governance, your self-service data analytics platform will be processing poor quality data.

Poor quality data will severely compromise your findings, making it much harder for business people to make decisions. Data governance ensures that data is of high quality and renders more accurate readings.

By contrast, data governance ensures that data is clean and consistent across the board. Furthermore, it’s been my experience that when there is a governance framework in place, organisations inevitably get better at organising the hierarchy of roles and responsibilities around managing data.

Setting the right governance framework

There are several ways for organisations to set a data governance framework that ensures compliance, transparency and better business outcomes. Here are just some of the methods you can follow.

Integrate self-service analytics with data preparation carefully

A key part of establishing a framework for governance is addressing data preparation. Organisations should look to integrate data governance with data preparation processes to meet compliance standards. Automation and web administration technologies can help tremendously in this process.

Make sure teams are trained

Self-service data analytics might be easy to use, but that doesn’t guarantee that everyone can use it, nor is it a guarantee that people with access will follow data governance laws. So it is important to keep everyone up to speed by providing some training sessions.

Maintain a balance between standardisation and agility

Self-service data analytics often runs the danger of being poorly coordinated. To make sure this doesn’t happen, organisations need to maintain a balance between user agility and BI standardisation. Some ways this can be done is by creating a self-service application with standard choices or offering guidance within self-service platforms.

Setting a standard in self-service data analytics

With data analytics becoming a bigger part of the organisation, self-service data analytics would become a vital tool to generate reports and answer business-oriented questions. So, it is important to ensure that the system follows data governance laws to ensure accurate results and that organisations are not unintentionally violating data regulations.

While this might seem like an extra step in the short-run, it yields tremendous value in the long-run.

However, while self-service data analytics is crucial for modern organisations, they are not quite up to the standard of more advanced analytics systems, like machine learning and NLP. To integrate those systems into their infrastructure, organisations should consult with SAS experts, like the Selerity team.

How a data governance framework improves data management

In the IT industry, it is well-known that data is more precious than oil. Yet, despite the immense value of data, many organisations do not make full use of their data. There are several reasons behind this – lack of initiative from executives is one example. But, one of the core problems I have discovered is the lack of a comprehensive data governance framework. With this framework in place, organisations address all aspects of their data management, ranging from their practices to technologies in use. In this blog post, we are going to take a look at the key essentials of a data governance framework and why organisations need to have one.

The value of a data governance framework

A data governance framework is a must-have for every organisation because it helps manage the growing volume of data. Data utilisation is growing at an exponential rate with organisations collecting petabytes of data daily. However, without a framework to collect, integrate, clean and analyse data, it is impossible to manage the growing volume of data and derive meaning from it. Data is immensely valuable, but like coal or oil, it cannot be used in its raw state, it needs to be cleaned and refined before it can be useful. However, without a framework in place, making full use of data becomes impossible to do regularly.

A data governance framework allows organisations to make a direct connection between data and KPIs or corporate drivers. The best way for organisations to generate the most value from their data is to tie it to company fortunes. That way, organisations can objectively measure progress on long-term and short-term goals. To tie data to an organisation’s fortunes, there needs to be a direct connection between data and corporate drivers. However, finding the connection can be very challenging without a data governance framework. An appropriate framework allows organisations to make direct correlations between data and corporate drivers like operational efficiency, profitability and costs.

As the public becomes more and more aware of data usage, there will be pressures to be more responsible and transparent in data usage. The first signs of data governance from government institutions can be found in the Sarbanes-Oxley Act in the US and the General Data Protection Regulation (GDPR) in the EU. In the future, I fully expect governments to keep a closer eye on how corporations use data. With a growing focus on data regulation, a data governance framework can be instrumental in ensuring that the organisation is complying with data laws.

The key elements of a framework

In setting up a data governance framework, organisations will need to reexamine everything from their policies to their attitudes towards data.

Categories for the data governance framework include but are not limited to corporate drivers, principles of using data, objectives behind data, groups for new data governance programs, methods for data usage, processes behind data usage, management structures, data management technologies and data governance methods.
A data governance framework entails a holistic shift for the organisation, which means several stakeholders need to work together. Executives and IT professionals in the organisation must cooperate to define the rules that will govern the use of data in applications. The two sides must define the use and management of data from data models, databases and even individual technology (for example, computers and laptops). They must also address processes and day-to-day use, especially for creating and using data. The key parties must also consider how the rules should be implemented so that rules do not hinder data creation and analysis.

How to get started

Building a data governance framework might seem like an impossible task, but organisations can take solace from the fact that these frameworks are not built from scratch. Most organisations, be it big or small, already have some sort of framework for their data. In most cases, the organisation only needs to adjust practices at certain steps or upgrade their existing technology. One such technology is the hosting environment. The right hosting environment is a tremendous asset in setting a data governance framework because it makes data management more efficient. Some organisations can even host their data on a cloud-based environment, physical servers or a combination of both.