As we move further into the age of data, we are finding ourselves in an increasingly data-rich and data-oriented world. Almost everything that a company does today is dictated by data analytics in one way or another. Many organisations have already begun using sophisticated analytics techniques to identify new growth areas, streamline costs, increase operating margins, make better human resource decisions and devise more effective budgets. Experts have also predicted that this influence will only continue to grow in the next few decades.
Despite its ever-growing influence, analytics is still a relatively new concept to a lot of people, particularly when it comes to buzzwords and key terms. And at the same time, there is no shortage of professionals looking to find a way to break into the analytics space.
In today’s post, we hope to inspire you to begin educating yourself in data analytics and use this to build towards a career in what is already one of the hottest skill-set of the 21st century.
Now, before we get started, let’s define data analytics.
What is Data Analytics?
Data analytics refers to the techniques to analyse data in order to enhance productivity and business growth. In this process, data is extracted from various sources and is cleaned and categorised to analyse various behavioural patterns. The techniques and the tools used varies according to the organisation or individual.
Check out Accenture’s video titled, What is Big Data Analytics?
Examples & Case Studies
Given how widely data is used in the world today, there are naturally going to be more than a few very interesting use cases highlighting how analytics has been used effectively by businesses.
One of the earliest adopters of data analytics is the financial sector. Data analytics has a huge role in the banking and finance industries as it’s used to predict market trends and assess risk. Credit scores are an example of data analytics that affects everyone. These scores use many data points to determine a lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce the risk for financial institutions.
Sales & Marketing
Let’s say you’re a marketer running an online ad campaign to promote a new smartphone with a better camera than the previous model. You might start by targeting the ad to people who bought the previous version of the smartphone.
As your campaign runs, you use data analytics techniques to sift through the data generated. By examining data about these users’ interests, you discover many of them are interested in photography. Using this information, you could fine-tune your ad to focus on users who bought the previous phone and like photography. You could also find new audiences of people who didn’t buy the older phone but are interested in photography.
The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding and improving diagnostic techniques are just a few examples of how data analytics is revolutionising the healthcare industry worldwide.
Netflix used data to create the immensely popular show House of Cards. They started by collecting massive amounts of data from their users, such as the kinds of shows and movies they watched and what actors and directors were the most popular. After crunching the numbers, they were able to identify that director David Fincher was among the most popular, as was actor Kevin Spacey.
Interestingly, they also found that the original British version of House of Cards was very well received by these same users. The numbers weighed very heavily in favour of combining these three elements (the show, Spacey and Fincher), which prompted them to buy the rights for the show and create an American version.
Did all this data work? The answer to that is a resounding yes! The show proved to be a massive hit and Netflix gained around 2 million new subscribers in the first quarter in the US alone. They also got a further 1 million new users from other markets around the world and this influx of new subscribers helped reimburse the production costs of the show.
Why is Data Analytics Important?
In a business context, the importance of business analytics cannot be overstated. It is used to examine and understand historical patterns and to predict and improve future business practices.
Although the applications of data analytics are broad, they can be used across various functions of an organisation. For instance, call centre managers analyse their team’s performance data for resource optimisation, HR managers use employee data to predict attrition and marketers use sales and marketing ROI data to decide the optimal allocation of their marketing budget.
As the importance of data analytics increases, it becomes more critical that organisations adopt data analytics. Here are 4 benefits of data analytics.
1) Improved decision making
Companies can use the insights they gain from data analytics to inform their decisions, leading to better outcomes. Data analytics eliminates much of the guesswork from planning marketing campaigns, choosing what content to create, developing products and more. It gives you a 360-degree view of your customers, which means you understand them more fully, enabling you to better meet their needs. Plus, with modern data analytics technology, you can continuously collect and analyse new data to update your understanding as conditions change.
2) Effective marketing
When you understand your audience better, you can market to them more effectively. Data analytics also gives you useful insights into how your campaigns are performing so that you can fine-tune them for optimal outcomes.
Using various data analytics tools, you can gain insights into which audience segments are most likely to interact with a campaign and convert. You can use this information to adjust your targeting criteria either manually or through automation, or use it to develop different messaging and creative for different segments. Improving your targeting results in more conversions and less ad waste.
3) Better customer service
Data analytics provides insights into your customers, allowing you to tailor customer service to their needs, provide more personalisation and build stronger relationships with them. Your data can reveal information about your customers’ communications preferences, interests, concerns and more. Having a central location for this data also ensures that your whole customer service team, as well as your sales and marketing teams, are on the same page.
4) Efficient operations
Data analytics can help you streamline your processes, save money and boost your bottom line. When you have an improved understanding of what your audience wants, you waste less time creating ads and content that don’t match your audience’s interests. This means less money wasted as well as improved results from your campaigns and content strategies. In addition to reducing your costs, analytics can also boost your revenue through increased conversions, ad revenue or subscriptions.
4 Types of Data
Data was almost exclusively structured in rows and columns and generally in a numeric form. Today, we have to deal with a wider variety of data such as text (reviews), images (photos on social media sites), audio (call recordings) and so on. These are considered to be semi-structured or unstructured data. While the possibilities are very exciting, these advancements present their own challenges to the data analytics process.
With any sort of data, the aim is to get it into a reasonably structured format. As a result, when data is discussed, we generally assume it to be in the form of a table, with rows and columns. In a standard data table, a row is called an ‘observation’ (referring to a specific, recorded data point), whereas a column is a ‘variable’.
There are different data types within structured data and it can be classified into three categories – numeric, character-based and date or time-based.
Numeric data is what most of us visualise when we think of data. It can be broadly classified into discrete and continuous. Discrete data is in whole numbers and cannot be further broken down. For example, the number of customers who visit a store on a particular day. Continuous data contains both whole numbers as well as fractions. This could include time, revenue, price etc.
After numeric data, the next broad category is character data. This is data made of text. Examples of this include brand names or classification of gender as male and female etc. In general, the idea is to apply a mathematical operation of some form in order to convert the data from qualitative to numeric.
This is not always possible, however, as not every character variable can be converted that way. For instance, while classifying gender, you can assign values of 0 and 1 to male and female respectively. However, some character variables are purely descriptive, such as a product name or a customer name.
Date/Time data is the third type of structured data. Date and time data are treated as a separate class of data in most statistical software, as they may contain character type as well as numeric data. It is expected that a mathematical operation may be performed on a given date (for example, the number of days since the last purchase). The main challenge with this type of data is for the tools to recognise them as dates and times, since they may be represented in different formats (with slashes, colons, words etc).
Semi-structured data may have some structure but it is often mixed with data that has no structure at all. Examples of semi-structured data are seen in what is called XML files. These are similar to HTML files. However, they contain information not just about how to display images or text on the web but also additional information about the type of object that is defined earlier. For instance, an address box will contain an address, a phone box will contain a phone number and so on.
As the name suggests, this type of data is completely lacking in any type of structure. Data requirements for unstructured data are naturally far higher than for structured data. When it comes to unstructured data, it’s not possible to automatically store this data in rows and columns and it would need a fair amount of processing to provide it with some sort of structure. This processing is reasonably complex and dynamic in itself. For instance, reviews on a site would each be in a unique style and with different tones and styles, something that an algorithm would struggle with.
Analysing unstructured data of this sort can be done in various ways such as by looking at the metadata and searching for specific things, such as a text string or a particular image. For example, to even categorise photos of men and women, you would need several thousand photos for the algorithm to reach a decent level of accuracy. As a result of this recent boom of unstructured data, it is important to know how to deal with it as a part of any data analysis process.
There are also instances where data is in a mixed form, containing both characters as well as numerical. Let’s take a customer ID as an example, which is BG7634-21. As this contains both texts as well as numbers, it will usually be stored as character data. In this case, depending on the specifications of the tool being used, the person performing the analysis may choose to separate the numeric or the character part.
At the end of the day, all data, whether structured or unstructured can and will be analysed. The only challenge with the latter is that the storage and analysis methods are significantly different from the traditional techniques used for structured data.
4 Types of Data Analytics
There are four types of data analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know – from what’s going on in the company to what solutions need to be adopted.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight.
Descriptive analytics describes what has happened over a given period of time. These techniques summarise large datasets to describe outcomes to stakeholders.
For example, when you visit the supermarket, there are certain items you buy every single time. Each time you buy an item, say a litre of milk, that information is recorded in the store’s inventory system. Over a period of a few months, the store can analyse this data to get an idea of how much milk has been sold, which in turn gives the retailer very interesting insight into the buying patterns of customers and supplies.
This is, quite simply, descriptive analytics. All the information is coming from the customer’s shopping basket and that data is just being sliced and diced and looked at from different angles in order to draw relevant conclusions.
Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesising. You use the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are also further investigated to discover why they got better or worse.
Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and their techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression.
Let’s use another example from the supermarket. Have you also ever wondered why milk is always at the far end of a store but magazines and chocolates are at the checkout counter? Through extensive analysis, retailers realized that while walking all the way to pick up your essentials, you might be tempted to buy something else as well. Plus, chocolates and magazines are cheap impulses buys so you’re likely to not think twice to put them in your basket, just before you ring up all your purchases.
This, in a nutshell, is how predictive analytics works – by identifying patterns in historical data and then using statistics to make inferences about the future. We essentially attempt to fit the data into a certain pattern and if the data is following that pattern, we can predict what will happen in the future with some certainty.
Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.
Prescriptive analytics is something that came into existence only about a decade ago and has since proven to be a very popular and powerful tool with businesses around the world. It can be used to analyse data in the present as well as predict what might happen going forward. Most significantly, it provides insights into what steps should be taken based on the available data and what the impact of these decisions would be. This tremendous versatility places prescriptive analytics at the cutting edge of analytics technology.
An excellent example of an industry where this technique is extensively used in aviation. Airlines companies are always looking for ways to optimise their routes for maximum efficiency, which can help them save billions of dollars. And so, the industry is constantly using prescriptive analytics to identify more efficient ways to operate, which can keep airline costs down and profits up.
Terms & Jargons in Data Analytics
Let’s take a look at some popular terms you are likely to come across in the analytics space.
Big Data – The term ‘Big Data’ tends to be the source of confusion for a lot of people, largely because of how it is used interchangeably with ‘analytics’. In fact, Big Data is more a subset of analytics as a whole, which can be characterized by the ‘3 Vs’ – variety, volume and velocity.
Business Intelligence (BI) – It is typically used to examine data and draw inferences from past events. While it is considered by many to be a lesser variant of analytics, a more accurate description would be that it is a component of the overarching field of analytics. The term ‘Business Intelligence’ is older and was in vogue long before ‘analytics’.
Relational Database Management Systems (RDMBS) – These are essentially how most business data is stored. Information is represented by tables and a “primary key” helps connect and understand the relationship between different tables. Popular examples include Oracle, Microsoft SQL, MySQL and others.
Structured Query Language (SQL) – This is a very popular language used to query databases. An organisation would use this to identify what its top products are in terms of unit sales in a particular location.
Data Warehouses and Marts – They are both examples of databases used and created for the ability to generate reports. Data marts are generally specific to a team whereas data warehouses would hold data relevant to the whole enterprise.
Data lake – A data lake is very similar to a data warehouse with the difference being that data in a lake would be in a much more “raw” form compared to what is stored in a warehouse. A data lake could contain all kinds of data, both structured as well as unstructured.
Extract, Transform, Load (ETL) – It is an important stage of data processing. It refers to the collection of data from operating systems, cleaning it to suit the procedural requirements and then loading the clean data into the appropriate warehousing system.
Challenges of Data Analytics
While data analytics can provide many benefits to the companies that use it, it’s not without its challenges. Working with the right partners and using the right tools can help businesses to overcome these difficulties.
One of the biggest challenges related to data analytics is collecting the data. There’s a lot of data that businesses could potentially collect, and they need to determine what to prioritise. Collecting data requires tools that can gather data from website visits, ad clicks and other interactions and deliver it in a usable format.
Once you collect your data, you need somewhere to store it. This can take up a considerable amount of space and contain many different types of information. You have to integrate both structured data and unstructured data from online and offline sources and from internal and external sources.
You also need to ensure data quality so your results are accurate. In addition, your data needs to be accessible and not siloed so everyone throughout your organisation has the same repository.
Lastly, you need to have the knowledge and tools necessary to make sense of the data and provide insights. Arm yourself with relevant knowledge, skills, and tools with our highly rated data analytics course – Problem Solving with Data Analytics.
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