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Here are the top five tools for data analysts -- and the key skills they need to 'predict the future'

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Predictive analytics is changing the way the world works, but it’s hard to know what tools are needed to make the most of this growing opportunity.

There is only one sure-fire way for organisations, both in business and government, to develop efficiencies and to innovate in a way that resonates with their market.

In order to achieve its goals, an organisation needs to be able to see the future.

In the past, this has been almost impossible. Industry leaders whose decisions miraculously matched with market forces – Steve Jobs, Warren Buffett, Richard Branson, etc. – have been few and far between. But, thanks to machine learning and predictive analytics tools, a data analyst can now give an organisation the priceless gift of future vision.

How does this help?

Predictive analytics reduces wastage and increases efficiency. It gives a clear indication of where resources are best targeted. It identifies upcoming trends, offers insight into products and services that will be in demand over the coming years and decades, and lets customer service teams know when customers will want to be contacted.

Among many other things, predictive analytics can identify great investments, help governments plan for future populations, identify fraud, save resources, ensure supply always meets demand and so much more. It does so by using machine learning and statistical modelling to examine past data and develop accurate models of the future.

Key skills a data analyst needs to be successful

It’s no wonder that a report by Markets And Markets says the predictive analytics market, worth US$4.56 billion in 2017, will grow to a value of US$12.41 billion by 2022. The region with the fastest compound annual growth rate, the report says, will be the Asia Pacific.

At UNSW, we’ve identified the top five skills and traits required by data analysts, which are:

1) Familiarity with the enormously popular and efficient SQL database programming language.

2) The ability to think critically and make connections between disparate ideas.

3) The ability to communicate the results of data analysis clearly with business partners and other stakeholders, and in the framework of the organisation’s strategy.

4) Fluency in several other database scripting and statistical languages, such as Excel, Python and R.

5) A broader understanding of the workings of business, and the pressures on other departments.

Five top tools for predictive analysis

There are several predictive analysis tools made for specific industries or sectors, such as BOARD, for insurance and banking. In this case, we’re more interested in tools designed for predictive analysts, which can be shaped and customised toward the needs of several different client types.

Here are some of the best:

SAS Advanced Analytics
Whether you’re a beginner or a seasoned pro, this analytics software thrives under the most difficult predictive challenges and offers easily digestible visual graphics. Very large data sets are no problem, and movement analysis is comfortably accommodated, allowing for various predictive models as situations or strategies change.

Statistica Decisioning Platform
Just like it says on the tin, the Statistica Decisioning Platform utilises predictive analytics to help organisations make better decisions. It looks at patterns and trends throughout the organisation’s history to figure out the best way forward and the most efficient way around roadblocks. It can be matched to almost any role, department, organisation or industry, it combines structured and unstructured data and its objectives include employee performance, customer retention, customer acquisition and more.

SAP HANA
Marketed as a public or private cloud solution allowing organisations to harness the power of their data for real-time decisions and actions, SAP HANA develops predictive models after being fed internal, organisational data alone or, for a broader view, data from any number of external sets. Predictive analytics libraries can also be added to the mix to help tame sets of big data and social networking data can be brought on board to predict customer trends.

RapidMiner Studio
It’s the ‘Rapid’ in RapidMiner Studio that the software developer promotes most heavily in this code-optional visual workflow designer. A drag and drop interface helps to accelerate the creation of predictive models and a library of over 1500 machine learning algorithms plays nicely with data from any source, anywhere. Data can be queried and retrieved in the database without writing SQL and a wide mix of machine learning techniques including regression, clustering, text analytics and many more, can be implemented.

IBM SPSS
Like many of the other top predictive analytics software packages, IBM SPSS offers an enormous selection of machine learning algorithms, text analysis and integration with big data. It can work with structured and unstructured data and can be implemented – in the cloud, on-premise or as a mix. Social media data and natural language processing help to capture vital feeds for predictive insights that lend themselves to powerful decision making.

Find out more about learning these skills and using these tools through UNSW’s Master of Analytics (Online).

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