An Introduction to Agile Analytics
That today’s businesses use analytics is no surprise, but the effort and time taken to reach valuable insights and results can be. Equally, I still get the feeling that not everyone in a business is fully briefed on what’s going on or what they could be doing about it. There are a number of reasons for this, but several of the oft-heard “reasons” may sound familiar: departmental duplications of effort, reliability and accessibility of data, inconsistent and hard-to-compare insights. It seems there’s just too many obstacles in the way.
Instead, do you sometimes find yourself imagining a world where your users have access to the up-to-date, trusted, agreed-upon view of information with the ability to combine this with their own sources of insight? Freeing up IT to focus on value add activities and empowering information consumers to realise new insights rather than just helping people with churning out reports. Combined with a simplified view on how to incorporate more advanced analytical techniques, SAP provides Agile Analytics for you and your organisation to do just that.
But, where does that desire to escape come from? Speaking with colleagues, partners and also with customers’ IT departments – the ones that oversee organisations’ technology use, there is a concern that individual teams use analytics in different ways, with different support needs and different times-to-insight. This often leads to confusion in the organisation (e.g. when comparing across teams or when scheduling collaborative reports) and hence not even considering the benefits of reuse or collaborative analysis. It’s obvious why there’s a reaction to just walk away from it all.
Whilst, for some, this may lead to a call to lock-down their analytics tools or to apply a one-size-fits approach to obtaining insights and analytical results. Agile Analytics, SAP’s more inclusive approach, recognises that there is a broad spectrum of analytical roles from the traditional power user, the data scientist, to more casual or infrequent users who may only need create reports for specific presentations. But this would be to focus only on generating analyses – Agile Analytics includes the realisations that there’s also a number of roles through which analyses can be consumed: detailed reports allowing operational decision-makers to run the business through to simple consumers (“insight surfers”) of the results be they fellow employees, customers or partners. And are these communities the same for every size of enterprise? Probably not.
Addressing the needs of these communities ensures that analyses (and data) can be shared as broadly as possible – getting as much value out of them as possible, across any size of company. The result of this is that the right reports and trustworthy insights are generated. People rely on and discuss the data productively. In essence, the organisation’s needs as a whole lead to a vision for analytics and reporting can be encapsulated by three keys ideas: simple, enterprise-scale and agile. This is what SAP empowers with Agile Analytics.
Simple in that there’s an obvious path to take from data management to content dissemination: teams can now collaborate without friction. Recognising that there’s phases to the process of analysis allows these common activities to be compartmentalised, allowing the users to focus on the tasks at hand. This makes working between teams (and adopting others’ resources) much easier, but it also enables a self-service approach to analytical tasks. Users don’t need to be trained up so much as they can enable themselves.
Enterprise-scale in that all roles of user can participate as desired in analytical activities across a broad range of analyses: statistical, predictive, time series and so on. Less “random exploration” of the data and more consistent approach to results, insight and decision indicators. Remember also that simple enables the enterprise to scale its capabilities: if you want your many employees to use analytics without incurring significant training and support costs, then you need self-service.
Agile in that users work on trusted data, re-use existing analyses and templates. Time spent searching and verifying data is often a major headache, so re-use and collaborative processes are key. Agility also means getting the results presented immediately to the relevant parties – enabling the business to make agile decisions or understands opportunities and risks in real-time. Getting relevant events and notifications sent to the right users’ devices are also part of an enterprise’s agility capability.
One area not yet discussed is that of devices: increasingly enterprises are finding they have to support multiple employee devices (especially post-acquisition), be it desktops, browsers, tablets, or phones. And, increasingly, employees are asking and are being asked to make decisions wherever they are and whenever they can. This can mean that IT has the often onerous and thankless task of considering and incorporating all these devices, their data and its security and various modes of interaction into their business agility plans. How does an organisation avoid complexity arising in that situation?
Over the next few weeks, we’re going to explore what Agile Analytics means for some of the key participants in an organisation’s analytical activities such as the casual user and the employee-consumer – how they interact with analytics and what they can expect to achieve, but also what it means for the enterprise as a whole. In brief, does Agile Analytics actually address any of that institutional madness that some traditional analytics approaches have allowed to flourish?