Business intelligence (BI) is nothing new. Even before computers came along, business owners would sit down and pore through the ledgers, looking at sales, returns, and — although it may not have been expressed like this — unearthed critical insights.

In an age where data throughputs in enterprise-scale organizations leave writers grasping at terms like “data torrent” and “exponential growth in information,” it’s interesting to note that BI software from even a few years back isn’t capable of processing all the data at hand.

Spreadsheet mastery as a skill is still much sought-after, and macros have the ability to virtually emulate powerful application code, but these solutions could easily be described as legacy platforms.

For many enterprises, that’s an ironic situation. The Harvard Business Review analytic services research team has found that 92 percent of companies believe that data analytics will be even more important in two years than it is now, and a majority (55 percent) think data analytics is extremely important today.

If business-focused analysis of data is so critical to the development of today’s digitally-driven enterprises, why is the data analysis process so mired in problems associated with capturing all of the available data, normalizing and parsing it, processing it and then presenting it coherently as relevant findings?

The vast majority of data analysis teams’ time is spent before the data process and presentation stages, with most data scientists acknowledging that the time and resources are split 80:20 – that is, 80 percent preparation and 20 percent meaningful or productive activity.

The reasons for this seemingly incongruous “wasting” of precious hours by highly-skilled individuals using expensive tools are varied but typically stem from the mismatch between the speed at which technology progresses as well as the collation, processing and presentation solutions in use.

New technology solutions are producing new data silos with every new cloud service spun up or IoT instance deployed that’s found to have business value. But the tools used to ingest all of this new data were designed a few years back, typically for much smaller and static data pools.

While API technologies and orchestration platforms are breaking down some of those barriers between silos (an example of technology making amends in the field), the end results in terms of data still require a great deal of cleaning, verification, deduping and normalizing – and that’s even before a single line of code can be applied to a single data set.

Insights that data analysis teams can produce, however, have placed such teams into most business functions in organizations across APAC and Australia for a good reason, and that’s despite the 80:20 ratio that doesn’t make comfortable reading for finance teams. So what might happen in terms of business transformation if that ratio could be upended, so analysis teams spent the majority of their time analyzing, sharing, collaborating on, and presenting their data and less of their time in locating and normalizing data?

That’s where Alteryx comes in. Unlike legacy standalone BI solutions or modules added to a larger ERP platform, Alteryx was created by data scientists, for data scientists in business today. The Alteryx platform operates on some basic principles that make the daily routines of data analysis more productive and focused on business outcomes, helping position data scientists as crucial parts of the organization’s strategic decision-making processes. It achieves this by adhering to the following:

Making the data analysis processes collaborative

– In a large enterprise comprising different business functions, there’s bound to be a degree of duplication of work. In data analysis, that’s especially wasteful. By creating a collaborative platform that catalogs every process, collation, tool, method, model, and publication, teams can save themselves hundreds of hours of work.

– With a self-service data analysis model, different business functions have access to a shared pool of information and tools. That means stakeholders in all areas of the enterprise can approach their tasks with what’s effectively a direct line to their desired outcomes. Data professionals can come at a problem with insider knowledge of their department, from human resources to logistics, operations or information technology.

Finding the right information quickly

– The Alteryx platform helps organizations pull together all data sources quickly and shortens the amount of time in getting to insights. By integrating archived data, live streams and all data silos, teams unearth the information they need at speed.

– Integration with the different platforms in use in business today means that the Alteryx platform is not duplicating functions that have already been invested in. By interfacing directly, the Alteryx solution extends return on investment to other on-premise, cloud-based solutions across business functions.

Modeling quickly and efficiently

– Analysis teams use a wide range of tools, depending on their particular workflows, preferences, and the requirements of their departments. However, by mapping analytics, publishing models to a centralized catalog and visualizing data along its progress (so as criteria are applied, the effects become immediately apparent), the enterprise gets the very best from its specialists.

– Predictive and spatial analysis becomes faster, using bespoke algorithms or by leveraging external mapping tools and third-party machine-learning arrays.

Open sharing
– With a range of visualization options, business professionals who are not data specialists can see and absorb information in pertinent ways that are relevant to them. Line-of-business professionals can use self-service data collation, leaving analysis teams free to move on to more complex and fruitful tasks, rather than having to reiterate on every data request.

– By taking feedback in a collaborative platform, the data analysis teams and individual data scientists are always up-to-date as to the role the business wants them to play, making insights more on-point. The Alteryx platform engenders a wider business understanding among data professionals and does the same conversely: Business decision-makers get to be aware of just how powerful data can be.

Alteryx offers a free trial of its platform, so even a small data analysis team can see for itself the way that this 21st-century platform works. Instead of relying on BI tools that are not fit for purpose, or were never developed as collaborative hubs, enterprises across APAC and Australia are using Alteryx to unearth the insights that are putting them ahead.