Home Asia-Pacific III 2015 The role of the data scientist in tomorrow’s business

The role of the data scientist in tomorrow’s business

by Administrator
Alec GardnerIssue:Asia-Pacific III 2015
Article no.:9
Topic:The role of the data scientist in tomorrow’s business
Author:Alec Gardner
Title:General Manager, Advanced Analytics,
Australia & New Zealand
PDF size:233KB

About author

Alec Gardner is General Manager, Advanced Analytics, responsible for the Teradata Advanced Analytics line of business in Australia and New Zealand, incorporating Teradata Aster, Big Data Analytic solutions and the Teradata partnership with key Analytic partners such as SAS. In this capacity, Alec heads an expert team of Data Scientists and Business Analysts.
Alec is a well respected Business Analytics thought leader and spokesperson for Teradata in the ANZ marketplace and is driven in his quest to ensure organisations are able to see value and drive change from the Data they capture.
Alec has 16 years experience in the Business Intelligence and Analytics market specialising in customer performance management strategy and execution. He joined Teradata in 2008 as Industry Consulting and Pre Sales Director for Teradata South Pacific where he built a market leading capability of Industry Consultants and Data Warehouse Architectures advisory group.
Prior to Teradata, Alec was with Business Objects. Initially based in the UK before moving to Australia in 2004, Alec worked on some of the company’s largest customers in Retail, CPG, Oil & Gas and Utilities.
Alec began his career as a maths teacher, specialising in Children with Special Educational Needs, before moving into the IT industry in the mid 90’s as a Technical Consultant at Capgemini UK where he worked with customers in the Retail, CPG and Automotive Industries.

Article abstract

The increase of complex data has driven the big data movement, in which analytics technology is adapted to handle extremely large and varied datasets. Now, a new and vitally-important professional business role is emerging: the data scientist. The main role of the data scientist is to extract value from data on behalf of a company. This value is usually manifested in the form of better decision-making and greater insights.

Full Article

The global data analytics market is expected to hit US$125 billion in 2015, according to market research firm, IDC, with spending on rich media analytics set to triple during the year. Such figures highlight that data analytics is becoming a serious business investment around the world.
It hasn’t taken long for data analytics to start playing a central role in business. From retailers using customer information to better personalise their shopping experience, to spy agencies taking down international crime rings, data analytics has established itself as a go-to technology for solving complex problems.
As the volume of available data grows, so too does the complexity of data analytics. As of July 2015, there were more than 13.4 billion connected devices globally, according to Juniper Research. This is expected to grow to 38.5 billion by 2020. There are such large volumes of data being produced by these networked devices, which together comprise the Internet of Things (IoT), that most companies don’t know what to do with it all.
Big data breeds new knowledge needs
The increase of complex data has driven the big data movement, in which analytics technology is adapted to handle extremely large and varied datasets. Now, a new and vitally-important professional business role is emerging: the data scientist. The main role of the data scientist is to extract value from data on behalf of a company. This value is usually manifested in the form of better decision-making and greater insights.
According to the McKinsey Global Institute, the use of big data is set to become a key basis of competition and growth for individual firms. The use of big data is expected to increasingly underpin new waves of productivity growth and consumer surplus. As such, the data scientist stands to become one of the most important elements in the future of business.
However, McKinsey also suggests that a significant constraint on realising the full value of big data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from big data.
Whether or not an impending shortage of data scientists is on the horizon, the value of such professional skills is beyond doubt for the business community. However, this is yet to be truly realised by the business world. According to the Teradata Data Analysis Index 2015, although 88 per cent of companies in Australia and New Zealand use analytics to make decisions based on data, nearly the same percentage (86 per cent) had no plans at all to hire a data scientist.
Data-based business solutions
There is a lot of hype around data science at the moment, and there are substantial success stories of data scientists plying their trade to solve important problems. The Teradata Index, for example, found that businesses mainly use insights to improve customer interaction. More than three quarters of businesses surveyed said they use data to reduce customer service issues and complaints. More than half said they were using insights to improve customer loyalty and optimise marketing. Optimising business operations, and helping organisations make better decisions, faster, are just a few of the key benefits data analytics can provide.
E-commerce giant, eBay, is one of the companies using Teradata technology to improve their businesses. eBay leverages data to drive the business forward in all areas, from reporting all the way through to optimising the experience for its customers. According to Darren Bruntz, senior director at eBay, Inc., the company can leverage its data to better personalise the site experience for customers.
A growing number of organisations, like eBay, use data analytics to gain better insight into their customers and their own business. This helps with the decision-making process. Of course, there are many ways to make decisions: gut feel; instinct; industry knowledge; and experience, to name a few. But companies can also make decisions scientifically by methodically collecting, analysing and interpreting information.
Framing the right question
This is where the role of data science comes into play: to apply the scientific method to decision-making. While all science depends on data, there are many ways to use data unscientifically. This could include selectively collecting data to support a pre-conceived hypothesis. Because data science often involves sorting through a great amount of information and algorithms to extract desired insights, a data scientist has no other option but to view the decision-making process through the prism of scientific methodology.
With sound scientific methodology, data can produce valuable answers. However, it is important to be able to first frame the right questions before going down the data analytics path. Otherwise, the available data is likely to be underutilised or even misunderstood. As such, an important aspect of running a successful data science program is selecting the right problem to focus on.
The problem should meet a number of criteria to justify the effort involved in developing a solution. Identifying the problem effectively is another area where a data scientist is essential to the process.
Criteria for candidate projects to frame a business problem can include: data availability; ease of execution; alignment with corporate strategy; appropriate fit to available technology; ease of solution implementation; and stakeholder support.
Having selected an appropriate project and problem, the data scientist can then help an organisation understand the project and clarify the questions to be addressed. Even though new and unexpected insights may be gleaned during the execution of a discovery project, it’s important to be as clear as possible about the initial business questions that are being addressed.
Once the business questions have been clarified, the problem-solving process can begin. The initial stage often involves designing and implementing a data collection strategy and making decisions about large-scale data storage and management.
How to get the best results from data
While problem-solving techniques vary widely depending on the nature of the problem, data scientists will typically involve certain best practice-based steps in the process of analysing data.
A usual first step for most data scientists to identify and select an appropriate analysis tool capable of handing the volume of data and executing the required analyses.

A data scientist will also typically possess a deep understanding of the different types of analyses available to solve the problem, such as: parsing; paths; graph; text; and clustering (both supervised and unsupervised). Having competence in the available techniques is, in itself, a multi-disciplinary task.
Reducing dimensions and variables is also an important step. In fact, dimension reduction is now one of the more interesting challenges faced by the data scientist. Much of the growth in data volume in recent years has been driven by the increase in the number of ways we collect data about people, machines, and the interactions between them. In statistical modelling terms, this translates into an explosion in the number of possible variables that could be included in a model. Reducing dimensions helps data scientists cut back on variables.
Likewise, accuracy can be improved by improving the model. Many problems require analysts to build predictive models. Having developed an initial model, the challenge for a data scientist is then to improve its accuracy through techniques such as variable augmentation or ensemble methods, which combine a number of different models into a single prediction.
Effective problem-solving also often requires visualisation. This can range from a quick-and-dirty scatter plot to a complex, animated sigma diagram. Building the right visualisation to reveal the relevant aspect of the data under consideration is no trivial task, and a structured approach such as the Grammar of Graphics by Leland Wilkinson is often useful.
Meanwhile, cross-checking and asking the question in different ways can help data scientists build confidence that the insights are real and action-worthy. However, without the appropriate communication skills, confidence may still be difficult to build.
Communication skills are important but complex with regard to data science projects. The person who can execute the analysis may not always be particularly competent at communicating the results to a lay audience. Ideally you want a good technician who is also a good communicator, or a good communicator with some technical knowledge.
Having identified a meaningful data science problem and successfully solved it, implementing the solution into the business is the final important step. This often involves negotiating with business owners, which requires the ability to persuade and lead.
Data scientists inhabit a wide-ranging discipline, comprising skills from in-depth computer programming to business analysis and stakeholder management. Building a successful team of data scientists requires an awareness of the entire range of requisite skills and hiring to maintain a balance of skills across the team.
With the right team of experts, organisations will be able to unlock significant value by making the growing torrent of digital information transparent and usable at much higher frequency. Data analytics, big data, and the data scientists that can help decipher complex information can keep organisations competitive and position them at the vanguard of the business world’s best practices.

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