The Information Economy is disrupting the way we do business, society has become more data savvy and we increasingly believe we should be able to measure and analyse just about anything.
Everyone has compute power and is connected, which means everyone is now a potential audience for information and analytics, and there is a growing need for highly social collaboration and knowledge networks that support knowledge emerging, rather than fitting into predefined taxonomies
So what does this mean for BI, and how do these macro trends put BI in a position for to change?
BI to date has largely focused on people inside the four walls, C-Levels, analysts, and to some degree managers, providing a single app that unifies all the information in an organisation, supporting experts in making decisions, and pushing those decisions out to an organisation.
That focus has served us well through the turn of the last century, and solved many of problems of the day. But that success has in fact contributed significantly to the macro trends above.
The current tools are optimised for a set of problems and a context that is increasingly low value and is ceasing to exist. So the tools, architectures and capabilities need to evolve.
Here are some of the high-level implications:
BI, meet UI/UX
As an industry, we are data people. We extract, transform, load, model, optimise, cleanse, enrich and otherwise fret over data. The user interface is usually a dashboard, a pivot-table or a tabular report. That’s all you need, right?
When your target audience is 10% to 20% of an enterprise, and is largely executives, managers and analysts, then sure. You train them to use those interfaces, and they do enough data crunching for the investment to work out. But when a lot of value exists in providing analytics in the context of decisions that front-line workers make every day, the user experience matters.
This is the big lesson from the consumerisation of analytics. The future of BI is on your iPhone, not one monolithic application that answers all questions. The average smartphone user used 27 apps per month. That laser focus means the users love them, and they are highly productive. Pervasive BI looks a lot more like that. And to get there, UI/UX trumps the data model.
Analytics for mere mortals
If data discovery has taught us anything, it has taught us that more people need more capabilities than we originally thought. It isn’t just 'self-service reporting' or 'drill-down.'
They need to be able to acquire data, enrich data, reshape data, perform discovery tasks, perform data quality tasks, publish reports, collaborate, perform advanced analytics, and they need it now.
Visual analytics did a great job of getting from prepped data set to cool dashboard. But what about all the data wrangling? You still have to call IT for that. What about advanced analytics? You still have to call a data scientist for that. What about curating sanctioned data sets and publishing those to other users? Call the BI Competency Center!
I’m not trying to get rid of IT, or BI professionals, or data scientists. But as analytics becomes more pervasive, these types of capabilities have to be provided in packages that enable business users to accomplish a baseline set of capabilities without help from IT. And that baseline can’t be 'export to csv, import into data discovery tool.'
Business users need to mash up cloud application data with on-premise data, prepare it for analysis, and share it with other users. And they need methods for applying advanced modeling techniques that guides them and helps them understand the implications of the output and when they should call a data scientist. Without these capabilities, the notion of pervasive BI is dead in the water.
Analytics goes social
BI tools follow a very traditional model of thinking. Predefine the single version of knowledge and distribute it out. Dimensions and facts define the bounds of your thinking. Folders, with one report per folder, limit your ability to search and share.
Even data discovery follows a model in which an expert develops a report and publishes it for consumption.
So far, the concept of social BI involves being able to comment on reports based on predefined data and views filed in those predefined folders. Maybe with a little desktop sharing added in, so that two or three experts can develop the report before they publish it. That model is ok for some things, but simply doesn’t keep up when the business landscape is fluid.
Social concepts are powerful for facilitating very dynamic, agile collaboration and knowledge creation. Don’t wait to assemble a dashboard; publish everything into a stream constantly so the entire group can access it in real time. Use concepts like voting, favorites, or likes to help determine relevance of content.
Enable comments and mentions to facilitate conversations. Use tagging to ensure that as new ideas emerge, they don’t get lost in an old folder structure. If BI is going to support dynamic decision making in fluid business environments, it has to enable teams to dynamically form, collaborate on problems, and be able to redefine what matters to the business.
BI outside the four walls
BI and analytic applications have huge impact when targeted to customers, vendors, field employees and others 'outside the four walls.' BI apps are becoming revenue generators in-and-of themselves in some cases. And in many business models, value creation is so dependent on entities outside your own organisation that analytics targeted to those entities is critical to operations.
BI architectures have not been friendly to broad-scale, external facing deployments. The architectures are built to lock customers in from the data tier up through the presentation tier, and typically include heavy legacy client-server components. And the band-aide has been 'mobile' components or web front-ends plastered on top of those architectures.
External facing BI requires a completely new architecture. Modern web-application and mobile application architecture patterns. A data tier is great, but let me bring my own as well.
The user interface must be flexible to meet very targeted use-cases, including embedding analytic components into other web applications. And they must have scaling models that IT ops can work with and the CFO will pay for.
BI, data-centric no more
The data problem is an important one that we continue to struggle with, but BI has been so data-centric that the tools and architectures the industry has provided is showing its limitations. As enterprises demand tools to meet emerging challenges, I see the BI industry heading into an inflection point.
The 'data is the only problem' approach we’ve taken in the past must give way to recognise the importance of UI/UX, increasingly enabling the mere-mortal business user, promoting vibrant collaboration and social capabilities, and reaching everyone with targeted analytic capabilities that fit their needs.
These observations are shaping the way we work and develop our offerings, and all involved in the BI industry should consider these trends moving forward.
Sourced from Charles Caldwell, senior director, Global Solutions Engineering, Logi Analytics