Data should be at the heart of how people in companies make decisions. With the growth of big data and new sources of information available to us, it can be easy to overlook how we all use data as part of our decision-making every day. The challenge for IT teams and companies is to not let this mountain of data prevent decisions from being made. The risk of “analysis paralysis” can be found in business cases going back many years.
Today, many business operations teams are swamped with data from many different data sources, across every layer of an organisation. How can we make the process of sifting through this data easier? Building a community by networking data together can help users more efficiently analyze data and create more business value from it.
Collaboration around data still has a long way to go. A recent Experian report revealed that 64 percent of CIOs believe their organisations still aren’t making optimal use of the data available to them. The reason for this is that teams often work in silos. For example, the sales team may use its own data, marketing departments work with their own internal and external data, and other departments may use data in isolation. These silos of data aren’t a problem when individual teams are serving their own needs with their own specific data. However, it misses opportunities for collaboration that can improve results for the whole business.
Collaborating around data is more than simply sending existing reports out to contacts in other departments. When data is shared in this way, it’s easy for information to become outdated quickly, or be used out of context.
Why a data community is important
Data has become one of an organisation’s most valuable assets, and encouraging more collaboration around it involves finding ways to connect people throughout the organisation. To encourage data sharing – and more importantly, collaboration – getting a data community together can help. At the same time, networking data sets together can aid people in enhancing the value of their insights by leveraging those of others.
The idea behind a data community is that it should help all business users who might require access to data as part of their roles. However, rather than simply being a group of analysts or other experts who might be called on to provide a report, this should be approached as a combination of people across the enterprise, data and tools that everyone can use. This networked approach helps share information and analytics results more widely.
Often, businesses have this data but individuals don’t have the right tools in place to utilise it. To help people within an organisation understand the data they have readily available, it needs to be accessible across the business without being dependent on a central IT team. This is where self-service data preparation tools are needed. These tools can help those who don’t normally work with data understand it and get value from it.
The idea behind a data community is that it should help all business users who might require access to data as part of their roles. However, rather than simply being a group of analysts or other experts who might be called on to provide a report, this should be approached as a combination of people across the enterprise, data and tools that everyone can use. This networked approach helps share information and analytics results more widely. Often, businesses have this data but individuals don’t have the right tools in place to utilise it. To help people within an organisation understand the data they have readily available, it needs to be accessible across the business without being dependent on a central IT team. This is where self-service data preparation tools are needed. These tools can help those who don’t normally work with data understand it and get value from it.
Alongside this, building and maintaining a central set of company data, which all teams or individuals can use, can help too. Those with more skills can be sources of trusted analytics for the whole network without being bottlenecks for others to gain access to the results. This combination of central governance and individual data can help everyone get what they need without slowing down the business by depending on the central IT team.
The next step to forming a data community is examining the skillsets of the employees involved. For example, those in charge of analytics in the enterprise can provide a focal point for businesses to encourage more use of data for decision-making. There will also be individuals in the line of business who possess a fundamental understanding of working with data. They can provide analytics leadership within their departments. This removes the burden on IT experts, who can provide support for working with data in the right way but without acting as a reporting factory.
The role of cloud in building data communities
One of the easiest approaches to improving collaboration around analytics is the format in which results get shared. Normally, analytics results will be turned into visualisations that can be sent as PDFs or spreadsheets. These files – while being fine for providing standalone information – are static documents that are not good for collaboration. They don’t necessarily offer the ability for individuals to use the data for their own analytics, or help others understand how the results were arrived at.
To encourage more collaboration and community around data, it’s worth looking at how to bring people into these analytics results. Rather than static visualisations or simple files, can you provide access to the data that was used to produce the result itself? By exposing people to the data, you can show how the result was produced and get more people looking at the thought processes involved in the decision.
By using cloud-based analytics rather than file-based analytics, it is easier to share interrelated packages of data. This shift to cloud as a platform makes collaboration easier, as the data can be securely hosted and governed while everyone can access the results from where they are. At the same time, this can also help make the data preparation stage easier, too.
Cloud-based analytics also create new opportunities for teams and individuals to collaborate around data. Some cloud analytics platforms are built on multi-tenant architectures that make it possible to deploy virtual instances of the data ecosystem. Instead of duplicating physical environments, people can work in virtual “sandboxes” – or tenants – that combine their own data with virtual instances of data in other tenants. This approach helps organisations increase the collaboration around data across the enterprise more effectively and with less risk than before.
The results of data community
Data is valuable to individuals and departments such as sales or marketing, but by creating a community, it can encourage more collaboration that benefits the whole organisation in how decisions are made and metrics chosen.
Data can help companies look at their supply chains and streamline their operations, as well as improving their sales. For example, retailers can use external weather data to help them predict what they might need in stock. While a hot sunny weekend in July is likely to see an increase in sales of BBQ food and drinks, a wet weekend in Autumn will see sales focus on comfort food instead. However, this kind of analytics will affect more than just the sales team. In addition to predicting volumes of goods to be sold, supply chain operations and shipping logistics have to be managed, too.
Whereas these kinds of predictions could be made at the macro level in the past, the availability of real-time data and analytics through the community can help each department or store within the organisation enhance its planning and optimise its approach. For each manager, the data sets would be the same; however, the results that are provided to them would be filtered and tailored to their specific needs to help them make better decisions.
This approach to analytics can have a big impact on customers, too. In a recent report by Salesforce on the “State of the Connected Customer,” 81 percent of consumers expect the same level of service every time they interact with a company across different channels – from purchasing, to speaking with the customer services team.
For organisations selling through a single channel, holding data on customers is fairly simple and can help them when they interact with the customer. For companies with online operations, mobile apps, physical store locations or affiliate partners that can all act as sales channels to customers, getting a more consistent customer view is more difficult. At the same time, linking up marketing and sales activities with supply chain operations across these multiple channels is difficult without consistent and up-to-date information.
For a data community, this morass of information can be less daunting because individuals can look at what is relevant to them and their role in decisions. Rather than analytics being too generalised so that it does not help, or so specific to one set of circumstances that others cannot learn from it, this approach can make it possible for individuals to discover what they need. In this example, looking at comparisons with other locations or channels that are similar in size or scope can help individuals benchmark their activities more accurately.
By federating all this information across a community, individuals can have greater control over the data that matters to them, while still leveraging insights produced by the data community. More importantly, analytics activities that lead to positive results can be easily shared. Thinking of these results as recipes for potential success can help. Rather than prescribing actions from the central team, it’s easier for recommendations to be demonstrated and the potential benefits explained.
Putting processes and policies in place
As data collaboration becomes more widely used across organisations, processes need to be put in place between departments and teams to ensure that they talk to each other. By using shared data, they can become a community, and by connecting people through a shared analytical environment, companies can make data and analytics more accessible to all users – even those who are not so data savvy – while reducing data silos and maintaining governance.
The definition of a community is that it should share or have certain attitudes and interests in common. In the case of data, a community can help everyone benefit from the data collected across the business – and use it to make better business decisions.
Pedro Arellano, Vice President Product Strategy, Birst
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