How Decentralizing Data Makes Analytics a Team Sport

How Decentralizing Data Makes Analytics a Team Sport

Working with data is a team sport. But the problem for many businesses is that not all the key players within the organization have access to the information they need to win. Highly controlled data is like throwing your team out on the field without giving them the same playbook.


Peak performance in data analysis requires a decentralized approach that gives all the key employees, teams, and stakeholders at your company access to the data they need. That's why businesses are taking an increasingly decentralized approach to data sharing — and making sure that the right people have access to all the information they need for data analysis.


Many sales and marketing teams, for example, are now coming together to use the same datasets to decide how to minimize customer churn, maximize retention, and increase revenue per customer. Sales and marketing have always been on the same team, but with decentralized data sharing, they can now collaborate more effectively.


To take advantage of a data-guided approach that incorporates decentralized data sharing between teams, you need to understand the limitations of a centralized model. You can then start building a data-sharing structure that works for everyone and, most importantly, that keeps your critical data safe and secure.


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How Centralized Data Hampers Your Team

The biggest challenge in data sharing within an organization is often determining which people and teams should have access to various data sets. Because data has traditionally been centralized within certain departments, most often IT, productivity and collaboration took longer. Historically, whoever owned the data had to process countless access requests from various departments. Larger strategic projects often took priority, forcing other teams to wait for their data access requests to be processed.


The resulting delay in collaboration between teams slowed down the process of data analysis — and that means slower response time for issues such as increasing productivity and maintaining customer satisfaction. For example, management may be trying to take a data driven approach to HR in order to optimize worker happiness and productivity. But if IT is bogged down with other requests, HR may not get all the key employee-related information it needs to implement an effective strategy in a timely manner.


Thankfully, on-premise artificial intelligence (AI) tools are taking gatekeepers out of the mix and making IT teams more comfortable with data sharing. Today's AI tools for data sharing and analysis have simplified user interfaces, enable access across enterprises, and derive more actionable insights in less time.

Giving All Your Key Stakeholders Access

Modern marketing departments are the perfect example of what's possible once the data access bottleneck is alleviated. Marketing departments are just one example of all-too-common data bottlenecks that exist in other departments as well. Organizations are tackling the challenge by decentralizing data, taking it out of the IT gatekeepers' hands to get the necessary analysis done in a shorter time span.


In fact, marketers are taking an increasingly science-based approach to their work because they have a plethora of data to work with. Data such as purchase patterns, social media behavior, and even location-based history from smartphones can be used to scientifically improve targeted marketing and overall customer experience.


Combining access to the right data and AI tools, marketing teams can leverage their insights for actionable next steps. Instead of just working on marketing-specific issues — like social media campaign performance, for instance — marketers can come up with insights that add strategic value in other areas like how to better up-sell and cross-sell or to minimize customer churn.


According to a recent survey by McKinsey, the highest performing companies globally make data accessible across their organizations, in addition to providing self-serve analytical capabilities. That means various teams and departments get more useful information than ever, enabling them to uncover practical insights without having a data science background.


Of course, it's important to note that access, in and of itself, won't provide winning outcomes for your business. You'll need the right analytics tools — and a strategy that ensures that critical data doesn't end up where it shouldn't.

Playing the Data Sharing Game Safely

As with any team sport, the more players you have on the field the more likely there's going to be collisions, miscommunication, and the occasional injury. The same goes for decentralizing data access. The more teams you involve, the more you'll have to account for data safety and security to prevent mistakes from being made that could jeopardize your important information. High profile data breaches and hacks are a PR nightmare that every company wants to avoid at all costs.


According to Kaspersky, to ensure that information entrusted to you by your customers isn't compromised, you need to know what data types are being accessed, which employees are using it, and how it's being processed and disposed of. Some of the steps they recommend are:


  • Creating an inventory of data assets used by teams or individuals


  • Auditing critical services used in data sharing for strong privacy settings


  • Setting guidelines for what data should stay internal and what can be in the cloud


  • Adhering to strict guidelines regarding who has access to which data types


  • Conducting regular security awareness training specific to data sharing


Finally, you can consider outsourcing some of your data analytics to service and technology providers to complement your internal capabilities. It's just important that external partners have secure IT environments and strike a balance with your existing teams.


Ki — an automated AI analytics platform — is one example of how anyone in your organization can benefit from an outside software technology provider. Ki provides a safe IT environment for data sharing and analysis, with a user interface that even non data scientists can navigate.


Decentralizing data makes it possible for you to form an all-star team from departments across your organization. Instead of in-fighting for priority access, everyone who needs data can access it as needed. When all departments are working from the same playbook, companies can make decisions based on shared metrics. Opening up data access makes everyone a key player on your data analysis team.


To learn more about how an automated analytics software can supercharge your strategies, download the '4 Step Automated Process Digital Brochure'.



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