What is Data Analytics & Why Does it Matter?

The ability to harness data is a powerful business tool. With the rise of big data, businesses need to proactively manage, protect, transform and communicate data to make wise decisions. What once was a manual process has thankfully become highly automated. Data analytics is what takes raw data and makes it usable for insights and knowledge to inform more effective decision making to reduce risks and costs, and to maximise opportunity and profit. 

If you’re a business leader, you are likely aware of why data analytics matters. We will dive into advanced tips you need to know about data analytics and its application. 

Coming Up

1. What is Data Analytics? 

2. Understanding Data Analytics

3. Why Data Analytics Matters

4. A Quick View: Types of Data Analytics

5. Applications of Data Analytics

6. What is Data Science & Applications of Data Science?

7. What is Big Data & Applications of Big Data?

8. Why Does Data Analytics Matter in Finance?

9. Data Analytics: How it Works 

10. The Changing Role of Finance Departments

11. The Bottom Line

What is Data Analytics? 

Data analytics refers to the process of inspecting, cleaning, transforming and applying data to extrapolate useful information. Data analytics has become a primary tool within business today as it is used for various types of decision-making processes. 

Data analytics can be applied to any size of data sets, but as time progresses, businesses collect big data or a high volume of information. This historical data makes analytics more accurate as techniques can be applied to predict the future. Data tools can pull data from multiple locations and sources. 

Understanding Data Analytics

Companies across all industries can benefit from data analytics. Manufacturing companies may use data analytics to assess workloads to ensure machinery is operating at maximum capacity. Financial companies can use data analytics to help measure market risk. Retail companies can use data analytics to evaluate customer satisfaction and predict retention rates. 

No matter what you use data analytics to measure, you will incorporate a process that looks something like this:

• Determine the necessary data to collect. 
• Collect said data (computers, online sources, personnel, environmental sources, etc.).
• Organise the data so it can be analysed (this is where software can replace the manual and disparate storage across spreadsheets and store all data in a centralised and secure location).
• Clean the data for gaps or repetition. Once it’s clean, it can be used for analysis. 

Why Data Analytics Matters

Data analytics is essential for the fast-paced business world. It helps optimise overall performance and can significantly affect the bottom line. This is because data analytics helps to identify inefficiencies that cause waste. Once identified, you can make the necessary adjustments to reduce costs and add to your bottom line. 

Data analytics can be used to:

• Assess risk (i.e. banking and finance organisations use data analytics to measure a customer’s credit risk and market trends)
• Find trends 
• Predict outcomes 
• Increase the speed by which you can make informed decisions 
• Gauge customer satisfaction 

A Quick View: Types of Data Analytics

Just like different pieces of data that serve other purposes, various data analytics techniques provide further insights. Let’s take a look at the main types:

Descriptive: If you have a question about something that has already happened, then descriptive analytics can help you answer it. Descriptive analytics is often used as a means to explain something to stakeholders. For example, it can track return on investment (ROI) and other metrics of past performance. As useful as descriptive analytics is, it’s best to combine descriptive analytics with another method like diagnostic to go deeper into why something has happened. Descriptive analytics will point out what happened, but you will need to explore the reasoning behind the event still. 

Diagnostic: Like a diagnosis, diagnostic analytics provides insight as to why something happened. They work hand-in-hand with descriptive analytics to further explain critical findings. If you take a look at key performance indicators (KPIs) and want to understand why something improved or got worse, then diagnostic analytics help to:

• Identify anomalies in data 
• Collect the data that helps to understand the changes
• Uses statistical techniques to help explain such anomalies 
  

As a concrete example, diagnostic analytics can help answer why your team may have missed its targeted goals in a given period. 

Predictive: Like the name implies, predictive analytics work to answer questions about what could happen in the future. This analytic method leverages past data to evaluate trends and estimate the likelihood of something recurring. Statistical analysis, regression and machine learning is used to make predictive analytics function. 

Prescriptive: If you find yourself in a critical position to make a decision about the future but feel unsure about what choice to make, prescriptive analytics can be a lifesaver. Prescriptive analytics works by finding patterns from large datasets and then estimates the likelihood of different outcomes. 

Applications of Data Analytics

Data analytics has countless applications. Here’s a preview of what it can help do across industries. 

• Healthcare: Data analytics can be used to serve patients efficiently and monitor the quality of care. Machine learning is being used to optimise treatments, patient flow and how equipment functions within the healthcare system—this analytics help to save billions of dollars globally. 
  

• Energy Management: Utility companies can use data analytics to track network performance across millions of data points within their network. This way, they can dispatch crews for maintenance, quickly address service outages and optimise energy distribution. 
  

• Finance: The benefits of data analytics for financial organisations is a long one. Finance companies and banks can use data analytics to help manage risk, optimise process flow, improve compliance and predict lifetime customer value, to name a few. Automation tools like SolveXia have gained popularity, especially in the financial industry as it can transform finance teams, reduce low-value manual tasks so teams can remove bottlenecks, focus on high-value tasks and provide deep and impactful insights using live data reporting across the business. Additionally, automation tools provide audit trails to improve compliance and meeting of regulations, reducing risk through its analytics abilities as processes are mapped and automated. 
  

What is Data Science & Applications of Data Science?

Data science is like a car that drives data analytics forward. It is the field that encompasses data cleansing, preparation and analytics. Data science combines programming, problem-solving, statistics, math and computer science to extract insights from data. 

Data science is used for the following (plus more):

• Targeted advertisements
• Recommendation systems
• Internet searches 
• Image recognition
• Airline route planning
• Speech recognition 
  

What is Big Data & Applications of Big Data?

Big data is defined as “large, diverse sets of information that grow at ever-increasing rates.” With the sheer amount of data collected, it tends to come in different formats and from various sources. This makes data cleansing ever-important before applying data analytics. 

Big data can be structured (managed information) or unstructured (doesn’t fall into a predetermined format). Data analytics tools like SolveXia can pull together both types of data and help to evaluate correlations between the information (i.e. demographic data and purchase history). Big data is good for analytics tools because it provides a lot of information to understand patterns and trends. However, if big data isn’t properly managed, then it can provide noise and overload. 

Data analysts and automation tools can help to organise and structure all data into defined categories. Then, it can be used for data analytics. 

Why Does Data Analytics Matter in Finance?

Finance relies on data analytics for various purposes. Overall, the industry is rife with risk and decision-making, both of which data analytics can inform. Data analytics uses machine learning to analyse information and massively reduce risks. Here’s what data analytics can do for finance companies:

• Risk analytics: Risks can originate from competitors, credits, markets and more. Risk analytics helps to monitor and prioritise risks. Data analytics can assess a customer’s creditworthiness and predict how markets may react to changes in regulations, for example. 
  

• Consumer analytics: Data science can transform customer behaviour into insights for business decisions. In finance, an organisation can use this to increase cross-sales or estimate customer lifetime value. 
  

• Fraud detection: The risk of fraud is ever-growing. Algorithms can be used to recognise anomalies and spot fraudulent charges or purchases to minimise losses. 
  

• Personalised services: Financial institutions service people from varying income levels and with various needs. As such, it helps to offer personalised services to enhance interactions. When customers provide data, institutions can leverage the information better to serve their clients through machine learning and business intelligence. 
  

Data automation tools like SolveXia can help finance departments thrive. Benefits of automation tools include:

• Shared business objectives: You can use clear visualisations to depict business goals. This way, everyone is on the same page and can perform their job duties with objectives in mind. 
  

• Financial reporting made easy: With just the click of a few buttons, reports can be generated and automatically sent to necessary recipients and stakeholders. Real-time reports can also be set up, plus it connects to all data systems, including legacy systems, and the automation removes data errors and makes data mapping and cleansing simple, fast and automatic.
  

• Data-backed decision-making: Data analytics provide your team with deep and accurate insights that help expedite the decision-making process.  
  

• Process standardisation: Processes can be automated to relieve the team of manual, repetitive work and reduce bottlenecks. Across the board, all processes will run smoothly as they are defined in the system and run seamlessly with no human interference. 

Data Analytics: How it Works 

To function, data analytics requires several technologies that work together. The main components include:

Machine Learning: A subset of artificial intelligence, machine learning allows the software to learn and automate models. It works to analyse mass amounts of data quickly and provide results based on models. 

Data mining: Data mining takes large sets of data and finds patterns. It’s through this process by which information can be gleaned to answer complex business questions. 

Data management: As briefly mentioned, data must be managed and cleansed to be used for analytics. Data comes in and out of organisations at light speed, so there must be a set standard to collect, store and address the quality of data before it is used in practice. 

The Changing Role of Finance Departments

Finance departments are evolving as markets and technology do, too. It’s become less about manual data entry and more focused on data analytics. Finance teams provide knowledge to key decision-makers in business so that organisations can achieve their business goals. Data analytics tools like SolveXia does the plug and chug for you so that your finance team can focus on high-level analytical tasks and support your business’ success. 

The Bottom Line 

Data analytics is at the heart of the future for every business. Successful organisations can measure, track and adjust their processes and decisions to remain adaptable in changing environments. With the aid of data automation tools like SolveXia, data analytics and decision-making becomes easy and seamless and drive more accurate and deeper insights to reduce costs and increase profitability.