Data Science vs Data Analytics
Data analytics is the process of investigating and asking structured questions of information, be it business information, medical test information or whatever field you are working in. Typically data analytics looks at historical data and uses a fairly standard set of statistical techniques.
How to use data analytics
Data analytics requires a knowledge of general statistics, a logical mind that can structure out a sound problem solving approach, alongside technical skills that will enable data to be manipulated to run queries.
Data Analysts' skills typically lie in well-known data analytics software such as Excel, SQL, Python and R. There are other specialist tools such as SAS but these are becoming less and less common since the growth in open source solutions.
The easiest way to think about Data Analytics applications is to think about the range of different job titles associated with the role. The list is long, but some of the most common include:
Business Analyst – supports strategic planning and review activities looking at understanding the performance of the business.
Market Analyst – performs specialist analysis of internal and external data to size and understand macro activities taking place in a given market.
Market Research Analyst – reviews and consolidates the results of market research (typically surveys) into meaningful insights.
Sales Analyst – tracks sales performance and investigates the drivers of success and or failure.
Financial Analyst – reviews the financial performance of the business and investigate historical cash flow.
Marketing/Media Analyst – runs campaign analysis to understand the effectiveness and return of a marketing campaign.
Customer Success Analyst – tracks the customer journey to identify where drop off is happening or drivers for attrition are occurring and identify which remedial actions drive the best results.
Pricing Analyst – understands the impact of price changes on customer sales and margin in order to optimise the effectiveness of promotions.
How to use data science
Data Science is harder to define. Data Science is more about looking for answers to help estimate or predict the unknown by asking questions of the data. This requires not only the strong data and analytics skills of the Data Analyst but extensive coding skills and often significant experience to support the problem-solving process.
The tools or skill set required of the Data Scientist include machine learning, software development, coding and a strong grasp of data mining and data operations.
Data Science and Analytics is used in business for a wide range of applications. These include:
Fraud and Risk Detection – identifying patterns in transactions and finance applications to spot fraudulent activity, money laundering or mitigate the risk of generating bad debt through risky loans.
Healthcare – everything from assisting in drug development research, DNA and genome science to medical imaging analysis.
Web Search – optimising the search results for users through super fast analysis of search terms and delivering optimal results.
Online Advertising – using the same techniques for search to deliver the most relevant ads to web users and encouraging them to click through to brands.
Product Recommendations – utilising vast repositories of historical user searches and transactions to recommend the right product to website visitors such as Amazon.
Customer Experience Management – tracking multiple behavioural triggers to spot potential signs of customer dropping off mid transaction or unsubscribing.
Advanced Image and Speech Recognition – in use in many consumer applications such as Facebook and Siri or Alexa as well as more serious applications such as border controls.
Airline Route Planning – optimising the routes and schedule for airlines.
Gaming and Augmented Reality – delivering virtual reality experiences that can change as the user improves or predicting a users next move in a battle scenario.
A key challenge that Data Scientists need to contend with is ensuring the models and solutions they design behave in a responsible manner. There remains a constant risk that unintentional bias can be built into the machine learning algorithms and these can have devastating impacts on individual lives and company reputations.
Data Science vs Data Analytics
The data science vs data analytics debate has been going on for years and is largely driven through misunderstanding. These two terms are often used interchangeably and rightly so because they sound very similar and to most people mean the same thing or relate to the same people.
There are however some subtle differences, which is worth bearing in mind particularly when, for example, you are setting out plans to build your own data capability or equally go out and hire skills from the outside.
Data science and data analytics are closely related skills or careers. It would be wrong to suggest one is better than the other, they require similar skill sets applied differently and if anything the main difference is probably the personality type that each discipline is suited for.
Data analytics is all about answering well understood questions that are generally looking to provide better business decision making. It uses existing information to uncover actionable data and tends to focus on specific areas with specific goals as we have seen in the wide range of different roles above.
Data science however is typically more about free form discovery of new solutions or patterns that drive innovation. The end result with data science is generally unknown at the start of the process and the outcome less certain. Much like other scientists, Data Scientists specialise in the exploration and discovery of new patterns, drivers and insights in usually very large and complex data sets.
Does data analytics come under data science?
Quite often people see the difference as one relating to experience – the Data Analyst as the younger, less experienced sibling of the Data Scientist. In some ways this is true as it is a not uncommon career path between the two disciplines. However, to misunderstand the difference can lead to missed expectations. Each performs different tasks and plays to their own set of inherent strengths.
For many businesses, especially at the start of their data journey, they will find that Data Analysts will be critical in building the foundations for data driven decision making. At some stage their requirements will likely extend into looking at ways they can seek out even more gains through delving into more complex data sets such as digital data, image recognition and free text interpretation to power their omnichannel experiences. At this point they will be looking for the skills of a Data Scientist to deploy complex machine learning based solutions to hunt for these opportunities.
We believe it is unhelpful to think of these skill sets as falling within a hierarchy. Each plays an important role in the application of data and it does not always follow that a great Data Analyst will go on to become a great Data Scientist or vice versa.