Understanding the Basics of Data Analytics and its Application
In the area of decision-making, data is the integral unit that renders the process sound and complete. Having a profound understanding of how it can be maximized would open new doors for the business and strengthen its growth for the long haul.
This blog discusses what data analytics is about, its know-how and the progression it undergoes to be a valuable insight.
What is data analytics?
Data analytics is an in-depth examination of data through specialized technology and automated systems. It draws out raw data which is then translated to patterns, metrics and trends that are relevant for making informed business decisions. Coursera further expounds on the concept by dissecting it into five phases:
- Identification of the data you want to analyze
- Collection of the data
- Cleaning of the data in preparation for analysis
- Analysis of the data
- Interpretation of the results of the analysis
Through integrations of machine learning and other digital capabilities, data analytics can now encapsulate a wider scope in the field that they can essentially answer the questions of how you previously performed, how and where you currently are, where you should head and what you need to expect.
If CFOs have a grasp on how to handle data in analytics, they acquire the power to transform the provided information into an apprehensible and coherent solution, thus resolving any existing pitfalls of the company.
What does data analytics look like?
Depending on the question you're trying to figure out, data analytics can take various structures.
Data sets come in unstructured, structured, real-time and many other forms. One way to make sense of them is by employing different techniques aimed at specific goals. Here are the four primary applications of data analytics and where they are positioned in the process:
- Descriptive analysis — this answers the question of what happened over a given period of time. It is more of a general preview of your past performance.
- Diagnostic analysis — building from the descriptive analysis, diagnostic analysis digs a little deeper to find out what caused something to happen.
- Predictive analysis — as the name suggests, the process now moves to the stage of forecasting possible scenarios. Predictive analysis looks into historical data to see if previous happenings are likely to recur.
- Prescriptive analysis — this area provides a data-driven course of action by using the insights from the predictive analysis.
How to turn data into insights
With a handful of business intelligence (BI) tools aiding your accounting team and finance executives, shaping data into a higher gear becomes inevitably simpler. Attempting to do so without the power of BI can result in an oversight of any valuable detail.
The process of turning data into actionable insights looks like this:
Setting your goals
You have identified the issues in the business. That's a great start. It's now time to set up your goal. What are you trying to answer or resolve?
Along with your KPIs, your objectives will serve as a structure you follow as you work towards the end of the process. It’s also good to note that in establishing goals and KPIs, following the SMART (Specific, Measurable, Attainable, Realistic, and Timely) framework will be conducive course.
Harnessing BI tools
After you have figured out what you want to achieve, the next step would be to select the ideal BI tool/s or software that best caters to what you need to do.
We recognize that there are varying degrees of accuracy especially when data doesn’t spring from the same sources. That’s why upon the collection, organization and combination of all relevant data, they are collated under a single model. Ensuring you have both accurate and quality data is important to land an impactful BI output. How do you do so? By making use of the different BI tools such as Microsoft’s Power BI, Oracle Analytics, Tableau, Domo, MicroStrategy, SAP Business Objects BI Suite and many others.
Using clear visuals to contextualize
In relation to the utilization of BI software, visuals are also a salient part of the data transformation process.
After eliminating common data handling issues, you can now translate it into a visually-engaging form. You can create graphs, charts, reports and interactive dashboards that break down data into more sizeable and understandable formats. In this stage, communicating the results of your data extraction becomes a more meaningful experience for data users who are not accustomed to accounting terms.
Translate information to insights
Aside from reporting what happened for a specific period of time, insights are directed towards explaining why things happened the way they did and taking advantage of these learnings as you move forward.
You can augment your visuals by explaining what the numbers mean. What’s the story behind the dip in performance? How did we generate a considerable amount of profit last quarter? By articulating every data story, you lead the team to a better understanding of the situation. In addition, the insights you deliver back up the strategy you propose, making it a comprehensive initiative to take on.
Leveraging what data analytics can do opens the door for organizations to reshape how they operate and strategically steer their business towards a fool-proof future. The insights drawn out from your business’ performance allow key executives to develop innovative and forward-thinking decision-making, compared to the traditional manner of basing decisions on guesswork and hypotheses, which are scant in accuracy.
Interested to see how data, technology and expertise work hand-in-hand? Let D&V Philippines give you a first-hand experience of that. Our business analysis and reporting services guarantee to help you improve important areas of your operations. Want to know more? You can grab a copy of our Business Analytics whitepaper here or you can schedule a free consultation with our in-house analysts to know how we can serve you better.