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Overcoming the Challenges of Analytics In The Workplace


NOVEMBER 13, 2017

Data Analytics Insight Marketing
by Stephanie Collis
Head of Marketing & Communications, Zetaris
Follow me on LinkedIn & Twitter
There’s never been a more exciting or challenging moment for the analytics industry. With every advance in any technology comes both new opportunities and new difficulties, and big data analytics is no exception. As the technology rises in prominence in Australia – now sitting on the same ‘must-have’ list as CRM and marketing automation platforms – and more decision-makers become aware of how it could transform their business, the pressure is on people across the analytics industry to become effective evangelists.

So how do you do it?

Where do you start?

If you’re looking to make a real connection with the people who decide how much of their company to throw behind big data, you need to understand their concerns in order to offer them solutions.
The 2016 Institute of Analytics Professionals of Australia (IAPA) Skills & Salary Survey provides a handy guide to troubles that are keeping business-owners up at night when it comes to big data.

So let’s take a closer look at what about big data is challenging Australia’s decision-makers and how analytics experts can overcome it.
Better data, faster

Key capability challenges facing Australian analytics professionals include a lack of time for innovation, concern about the speed of access to high quality data, difficulties in prioritising tasks and allocating resources, and problems developing skills in new areas. While these sound substantial, often a fresh perspective and a bit of lateral thinking is all that’s needed to overcome these issues as fundamentally, these concerns boil down to analytics simply taking too long.

The roll-out of new technologies is changing the way that companies interact with their data for the better. Time-consuming extract, transform and load (ETL) processes – which became the way of doing data warehousing more than four decades ago and can cause corruption during the migration process – are finally being replaced by smarter, less intrusive and non-destructive ways of analysing data, such as virtualisation. Why wait for the data to be laboriously pulled from the repository, cleaned and business rules applied, and then published to target tables when data virtualisation provides better results sooner and with less chance of truncation?
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IAPA Skills & Salary Survey 2016: Challenges faced around data analytics & insights

Putting hours back on the clock

Another area where companies frequently waste their most precious resource – time – is in the filing and categorisation of data. Most older decision-makers are still locked into the idea of data as something that has to be tagged and organised as it comes into the business’ possession. In the same way that paper records need to be collated, bound and properly filed as they are created or received – otherwise they’d never be found again – many business-owners require all data to be categorised on arrival.
With some experts estimating that only 0.5 per cent of all stored data in the world is actually analysed, this is extremely wasteful. When only 1 in 200 documents is ever going to be referenced or see the light of day again, taking the time to lovingly collect and file the other 199 is wasted effort.

In data warehousing, this old-fashioned way of doing things is referred to as ‘schema on write’, where any new data is tagged and filed as it is created. An alternative – rising in popularity thanks to systems running on Hadoop architecture – is schema on read. Under this system, data is stored without needing to know its shoe size, maiden name and family history. Some metadata is retained so that the data can be looked up again, but it allows much more freedom in what is stored – everything from structured data from relational databases down to raw images and unstructured PDFs can be chucked in the same bucket and found again when or if it is required.

These changes can go a long way to freeing up employee’s time for crucial tasks such as developing their skills or innovating in their field, allowing your business to think while it sprints.

Making it all understandable
But ultimately, it’s not your decision. Demonstrating the value of something as complex and as potentially expensive as big data analytics is one of the biggest challenges facing analytics experts. While the 2016 IAPA survey indicated that great strides have been made in executive-level understand of data and analytics, getting organisations to act on insights provided by analytics, and in convincing organisations of the value of analytics, work still needs to be done.
To get more people onboard, you need to talk in a way they’ll understand. Frankly, most decision-makers aren’t techies, and aren’t going to be swayed by reams of technical specifications. Fortunately, the number of platforms designed for use by people who aren’t black belt data scientists is exploding. These systems are valuable not only in that they democratise analytics and make it a more accessible field, they allow every employee to work smarter and faster, pulling down insights as they require. Often, these systems can speak for themselves, requiring much less explanation than the more complicated and user-hostile systems of years ago. You’ll be able to rapidly demonstrate how much faster given tasks could be achieved when your employee is backed by a state-of-the-art analytical virtual data lake running on a distributed network.

Zetaris is the perfect system for helping your business get to grips with the power of big data analytics, offering real-time analytics that helps you understand your data faster, and providing actionable insights that pulls key takeaways out of petabytes’ worth of information. If you’re looking for a remedy to the problems highlighted by the 2016 Institute of Analytics Professionals of Australia Skills & Salary Survey, start a conversation with a Zetaris consultant today.


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