The role technology plays in almost every sector and industry is now enormous. From the impact of ecommerce and social media to improved telecommunications and digital inventory control, while technology can make daily operations much easier, it does generate a huge amount of information in the process. When analysed and utilised in the correct way, this raw data can yield invaluable insights that your business can use to make better data-driven decisions, become more efficient, and generally develop as an organisation. For this reason, investing in specific data analytics tools has become the difference between many businesses succeeding and failing in recent years.
However, what is actually meant by the term ‘data analytics’? What problems can these tools solve? And how does data analytics work in practice? In this blog post, Helastel will answer all of these questions and more as we take a deep dive into the world of data analytics.
What is meant by data analytics?
In its most basic form, data analytics is the discipline of extracting useful insights from raw data that can help with future data-driven decision-making. The term ‘data analytics’ itself is an umbrella term that is used fluidly to describe the tools, techniques and all-encompassing processes that organisations and individuals use to collect, organise, store and ultimately better understand the data they collect.
This important field draws from a wide range of different skill sets and corporate disciplines – including mathematics, statistics, computer programming and marketing – in an effort to ensure all aspects of a certain data set is properly analysed and useful information can be taken to improve business performance going forward. As well as encompassing several disciplines, typical data analysis processes also make use of a broad array of data management techniques. These include everything from basic data modelling and presenting methods to data mining, data warehousing, data cleansing and data optimisation.
As we will look at in more detail below, the main objective of data analysis is to use technology to find trends in harvested data that can be used to solve problems and form the basis of strategic planning. By doing this, future decision-making can be done using data as a justification, helping to produce better results and giving decision makers a way of showing stakeholders how and why certain decisions are being made.
What problems can data analytics solve?
Naturally, the problems that data analytics can be used to solve will depend on the type of business in question and its specific needs. However, as a rule, there are a number of key areas that data analytics can help with.
- New efficient solutions to old problems
Many problems businesses face are not new – they are pre-existing issues that have been, often poorly, managed by other, less effective or inefficient techniques. A good example of this comes from the world of professional baseball. As described in the book and film ‘Moneyball’, prior to the 2002 season, professional baseball teams in the US signed new players based on the collective wisdom of ‘baseball insiders’, such as scouts, former players, managers and coaches. However, when the Oakland Athletics successfully started using data science as part of their player recruitment processes, the team was able to build one of the most competitive teams in the country, despite having a limited budget. This model is now universally used in professional sport, and business more widely.
- Marginal gain improvement
A vital principle of management, small improvements to existing processes can help solve larger problems, making a business more streamlined, efficient and competitive. For example, retailers may update their target demographic on a monthly or even weekly basis based on ever-changing data. Being able to change tack, even just slightly, based on analysed data can solve the problem of being outperformed by competitors, allowing your business to always stay one step ahead of the crowd.
- Improved marketing
If you can better understand your audience, you can target your marketing accordingly. Data analytics prevents the problem of wasting marketing budget on unfruitful areas in the future by providing useful insights into how your campaigns are performing. This allows you to fine-tune future marketing practices.
- Improved customer services
Data has the potential to reveal valuable information about your customers. This will not only impact how you choose to market your brand, but can also help you to understand preferences, interests, and concerns. In turn, this can help to solve the problem of ineffective customer service, allowing your business to tailor your services to specific needs, building stronger relationships in the process.
How data analytics works
Although data analytics processes will differ from business to business, they generally follow the same four steps.
1. Data collection
First businesses gather data – both structured and unstructured – from a wide range of sources. These could include cloud data storage, online data collection points, mobile applications, in-store surveys etc. Structured data that is ready to be analysed may be put in a business’ data warehouse at this stage, where business intelligence tools can easily access it.
2. Data processing
After data is collected, it must be organised. This can be done in two ways – batch processing and stream processing. Batch processing involves looking at large blocks of data over a period of time and is ideal for achieving long-term data project goals. On the other hand, stream processing uses complex computer programming to organise data directly as it is received. This method is more expensive, but can yield much faster results.
3. Data cleaning
Once correctly organised, data must be tidied up or ‘cleaned’ in order to make it as easy to analyse as possible. This process will involve ensuring all data is formatted in a uniform way and making sure there is no duplicate or faulty data in the set. If this is not done, future analysis can be flawed, leading to misleading insights.
4. Data analysis
At this stage raw data can be turned into useful insights. There are four types of analytics:
Descriptive: these methods use raw data from multiple data sources to provide valuable insights into the past.
Diagnostic: these methods help you to understand the cause of past events.
Predictive: these methods use data to predict what is most likely to happen in the future.
Prescriptive: these methods recommend actions you can take to impact predicted outcomes.
While different types of analytics can cost more than others, with some requiring outsourced specialists and/or a team of in-house, full-time experts to manage, even the most basic forms of data analysis can help a business gain a competitive advantage over rival companies. Although it may take a period of trial and error, this means finding the best types of analysis to suit the needs of your business should be a priority for any business looking to take advantage of data analytics.