In today’s hyper-connected digital world, access to data has become a top priority for organizations of all sizes. But as more businesses look to their own data to derive insights, they’re slowly realising that just collecting and analysing their own data can sometimes fail to generate game changing customer insights. To create true value, data insights need to be collected and compared against data sets from other organizations which provide unseen context and customer perspectives. And yet, this isn’t happening.
Unfortunately, many businesses haven’t yet developed the necessary framework to either collect or share this type of information. Internal processes restrict analysts from being able to do their jobs effectively, while a lack of vision stops businesses from seeing the opportunities inherent in connecting their data with that of other businesses. Ultimately, companies are either too slow to collect data themselves, or unable to partner with others.
The ebook you’re reading is designed to help shift perceptions around this status quo. It describes how organizations can create new data governance structures that define the collection of useful data, ensure its secure storage and position it as a strategic asset. It also details how businesses can begin setting up systems within their own walls to investigate this data, uncovering new insights independently
- Tips to structure your data governance team
- Insight on how to build a privacy by design system
- Guidance on how to leverage data as an asset
Data is one of the most lucrative commodities of the 21st century. Without a structure to make that data useful, businesses will fall behind. But there is a way to ensure this doesn’t happen: through sound, secure and effective data governance.
Data governance: How do you stack up?
Businesses and organizations produce more data than they know what to do with.
In fact, the International Data Corporation (IDC) predicts that by 2020, the amount of high-value data that is worth analysis will double. However, despite sheer volume and quality, data is very rarely utilized effectively.
This isn’t due to a lack of effort. Many businesses simply don’t have the resources or expertise to handle it all – which is exactly where data governance (DG) comes in.
The idea behind DG is to create processes, policies and structures anchored in leveraging data as an asset. This can be for information ranging from product analysis to consumer behavioral patterns – it’s entirely adaptable depending on the organization and its data set.
Some businesses are committing to this approach by creating teams known as Data Governance Councils. Made up of employees from various departments, the aim of a council is to provide co-operative guidance on how data is mined and used.
These councils have enterprise-wide authority over data management and infrastructure development, ensuring that data governance activities align with strategic business goals. Taking it a step further, many forward thinking businesses are even creating specific roles for those overseeing the collection and use of data.
For a business hoping to establish a similar internal governance framework, it should always ask the following questions:
- What is the business’s purpose in collecting data?
- What are the goals for any use of data?
- How does the use of data align with the organization’s overarching goals and mission?
- What policies will be in place for how data is collected and used?
To be effective, the Data Governance Council should be a cross-functional team with representatives from all relevant areas of the business. It must also be afforded as much influence on the business as a human resources team would have, defining how every branch of the company approaches data and its analysis. This cultural shift requires executive buy-in and support for data governance initiatives.
The path to safer, better data
One of the biggest problems businesses face when establishing a council to oversee data operations is that raw facts and figures are rarely analysis-ready.
The information is often painstakingly generated and then held in numerous disjointed databases with no structure; bits and pieces are missing, and there’s no collection protocol in place. Unfortunately, data is also often stored with a complete disregard for safety. Governance on behalf of a council can help solve these issues.
The best place to start is with the adoption of a “privacy by design” policy. This approach, endorsed and promoted by the Office of the Australian Information Commissioner, creates a framework by which every product, service or system is built with privacy in mind from its inception. The value of this approach is often underestimated.
Instead of safeguarding data from the beginning, many companies treat security as little more than an afterthought – making information easily accessible to hostile threats. However, if products are built with privacy in mind, the issue becomes less about what security protocols are effective and more about the resilience of the product or service itself.
Businesses should seriously consider creating template applications for teams and individuals within an organization that require data access.
Changing an ingrained process to incorporate this idea isn’t easy, but it certainly saves headaches in the long run. That’s why many businesses have begun adopting the approach.
Perhaps most notably, Apple’s use of privacy by design has seen the company use a tokenization system for customers. This helps the tech giant avoid keeping credit card data on its own servers.
Once privacy protocols are firmly established, the council – or any alternative governance structure – should aim to create policies around the collection of data. It’s crucial that permissions are not given on reputation alone. There should be no shared access between distant colleagues and friends. Rather, employees should be given individual (or team) access to the specific data set they need for relevant projects.
As a result, businesses should seriously consider creating template applications for teams and individuals within an organization that require data access. These applications should consider their purpose, goals and a business case – ensuring that access to data is seen within the business as a serious endeavor.
By creating these structures, businesses are not only ensuring their own data protocols are secure, but are also working towards a collaborative effort. If data is effectively safeguarded, then there is greater opportunity for sharing with other companies for better insight.
Positioning data as an asset
Undoubtedly, innovation in data is an exciting prospect. However, for many leads and analysts, the benefits can be difficult to address in a tangible way. This often creates a barrier to getting senior leadership on board.
Change requires several steps, but begins with repurposing how we talk about data. Businesses need to create a culture in which data is an asset, not a limitation or security liability. It should be viewed as both accessible and lucrative when used properly.
If companies implement improved security protocols and effective governance structures (as we discussed in previous chapters), the general cost of handling information can reduce and its value will begin to be understood. In particular, creating a Data Governance Council will help promote data as worthy of attention from all areas of the organization.
Change requires several steps, but begins with repurposing how we talk about data.
Perhaps just as importantly, analysts and leads should reorient their internal and external discussions on data to focus on finance and profit.
It’s commercially necessary for a data lead to view data as an opportunity, not a cost or risk. When speaking with a CEO or leader, data should be positioned as a profit center with the potential to deliver massive returns. This can be emphasized by encouraging different departments to present a business case whenever they need access to information.
At this stage, it’s important to note that not every aspect of data governance needs to be housed within the company. Outsourcing certain data responsibilities, such as the creation of a framework to exchange data with other groups, is a perfectly valid and relevant discussion for data leads to have.
Reorienting the conversation around data to focus on opportunity requires a cross-collaborative effort, with every area of the business adjusting how it looks at data to focus on financial opportunity and reduced risk.
Designing to leverage data
Creating systems and processes around the use of data is one thing, but businesses need to ensure their infrastructure and teams are ready to make use of the information available.
As better, cleaner and safer data becomes ready for use, employees must be trained in how to leverage that information to improve existing products and services.
This ebook has already outlined some strategies:
- Establishing councils
- Surveying an organization for each team’s specific data needs
- Outsourcing data efforts
But more can be done. Businesses should train their people to leverage data in ways that will cultivate results.
All departments should be better educated in how to handle data properly.
Although a dedicated team may be appointed to manage data requests, it’s imperative that employees can handle that data once it is presented to them. This means training in basic software tools to help parse information and find trends.
Some sophisticated tools are free, and a large amount of training information is available online. Smaller businesses in particular may not have the resources to hire sophisticated coding teams, but training employees in free software tools can help strengthen their data analysis capabilities.
Training should also be implemented to help employees understand the basics of data analysis. While giving people tools and equipment is all well and good, it helps to differentiate “signals” from “noise”.
Understanding basic concepts like outliers can go a long way in stopping people from using data ineffectively. In this sense, businesses should encourage their teams to experiment with as much data as possible.
There’s no such thing as too much data– as long as the data is relevant.
Training should also be implemented to help employees understand the basics of data analysis.
Businesses should also promote hypothesis-driven activities.
Data is a great tool, but it’s just that – a tool. Without proper understanding it can produce terrible results.
In order to make business decisions based on data, employees should use that information to create a testable hypothesis that can be measured against their original theory.
This helps produce an evidence-based business culture that eradicates unreliable guesswork. Championing hypothesis-driven data analysis and experimentation means that when something goes right, companies have a good chance of knowing why. And this success can be repeated.
Finally, from an IT perspective, it should be easier for employees to share data within their teams. If the correct software isn’t available for effective cross-department communication, investments should be made to establish an infrastructure. If there is a lack of official policy, teams end up developing their own tools – which are often incompatible.
Generally, having an official approach ensures better collaboration.
Creating a data governance structure isn’t easy.
For some businesses it may even require taking an entirely fresh approach to existing products and services.
But the benefits aren’t just for the short term. The amount of data businesses are forced to interpret is only set to increase, and successful companies will soon stand apart from those who are unable to harness key strategies from their own data.
Businesses keen to learn more about data sharing and how they can improve their own processes can read more at the Data Republic blog, which publishes a weekly round-up of data insights and other resources on how to improve the treatment of internal data.
Data may be overwhelming, but it’s an opportunity – not a setback. Position your organization to take full advantage of it, and you’ll reap the many rewards.
Data may be overwhelming, but it’s an opportunity – not a setback.
Source: Data Republic