The ease and relatively low costs of storing the ever-increasing amounts of data being collected by businesses and organisations and the increasing use of channels, including social media, to acquire data in the first place, enable businesses to learn more about their customers, prospects and the marketplace in general.
This mountain of data being gathered is only of value if:
- The specific information the business requires can be drawn from it
- The data is collected and organised in the most appropriate fashion
This is where data science and data analytics – two distinct but heavily connected disciplines – come in.
What is Data Analytics?
Broadly speaking, data analytics is primarily focused on point 1 above: achieving a specific objective. An example of real-world use would be analysing customer buying habits to offer them relevant and associated products and services in the most effective way.
A basic example of this is Amazon’s ‘you may also like this’ method of showing associated products on the page a customer is visiting.
What is Data Science?
Data science is a wider term to include not only data analytics, but covering how data is gathered to start with, along with how it’s organised and what methods of analytics are used.
The difference between the two is best summed up with a house analogy: data science is the general ‘umbrella term’ of data gathering, storing, manipulation, analytics and covers the tools and techniques used – in effect the whole house. Data analytics by contrast is one room in the house such as a focused activity where analysis is centered around a particular goal such as the ‘offering customers related products’ example above.
Data analysis and data science may be handled in-house with the appropriate professionals on the staff, or contracted out to a specialist data analytics company who use advanced techniques and technology such as Microsoft Big Data solutions.
Does the Difference Between the Two Matter?
Professionals in either field – a data scientist or data analyst – perform different roles so it’s important to understand what data services you require either internally or from your data analytics company.
For example, you may decide you need to beef up your data handling capability due to the increasing data you’re gathering; therefore you might consider instituting some form of machine learning capability and developing certain algorithms.
This is where data science would play a major role, so you might choose to use the services of a company offering data science for business services or, if recruiting for staff positions, search for data scientists as opposed to data analysts.
Understanding Data Science and Analytics
Take for example a company wishing to use chatbots in their business to handle customer service and sales enquires.
Chatbot technology involves the use of AI (Artificial Intelligence), machine learning and big data. To set this up data science would be deployed by recruiting data scientist professionals (or briefing those already employed), or engaging a company providing data science for business services.
These professionals would use the above technologies, and perhaps others such as Microsoft Big Data solutions to set the chatbot facility up in a way that the type of data it will collect is harvested, stored and manipulated in the most effective fashion for a business’s particular requirements.
Once the chatbot facility is up and running, then data analytics as a specific activity takes over to literally ‘analyse’ the data streaming in from the chatbot activity. So for example, customer information can be assessed such as how often someone buys, what they buy, what their demographic is, and much more based on the information you want to acquire through using the chatbot set up.
So while data analytics and data science certainly go hand in hand, both clearly differ from one another so it’s important to understand what you require. It may be ‘only’ analytics so – if you don’t have data analysis experts on your staff – you may ask a data analytics company to help.
Alternatively, if you’re setting up data gathering in the form of chatbot technology or another method, then professionals who provide data science for business services would be appropriate.
That said, you’re likely to find data analytics providers also offer data science services.
How to Implement These Processes
You have the choice of either recruiting data science and data analytics professionals, or using the services of a data analytics company who can put the full weight of current analytics and data science for business expertise at your disposal.
The advantage of working with a specialist company is they can provide years of experience tailored to meeting your specific business needs without you having to go through the ‘time lag’ of the recruiting and learning curve process for new staff.
Experienced data analytics professionals will have worked on a multitude of projects serving various business types, so should be able to bring relevant field experience to your situation along with the latest techniques and technology – for example Microsoft Big Data solutions and Power BI (Business Intelligence).
Making Best Use of Your Data
In the same way having a powerful supercar isn’t much use without the key, your data isn’t serving you if you don’t gather, organise and analyse it to best effect. Modern data analytics and data science for business is the key to unlock the treasure trove of valuable business intelligence your data can provide you with.
Since data gathering will continue to rise as technology and techniques develop further, then using effective data science and data analytics – and understanding the specific roles both play in business – is only going to become more important to make the most of future opportunities and to stay competitive.