Data Analytics Company IndonesiaD
Data Analytics – Some data management experts have described Big Data as “huge, overwhelming, and uncontrollable sum of information.” Over the years, it has been transforming industries and changing human culture behavior, thus making it the biggest and most important assets for almost any organizations nowadays.
In recent years, data and its analytical capabilities has progressed and evolved to answer today’s business needs and challenges. The importance of big data, however, does not relies on the size of data a company has, but rather on how the company utilizes the data they collected.
Every company utilizes its data differently and for different goals. The more efficient they use it, the more beneficial it will for them.
In the previous article about Real-time Data Processing Architecture, we have been briefly introduced to Big Data and its characteristics.
In this one, we will learn further about Data Analytics and how having a data strategy is a smart move for your business.
Data Analytics: What is it?
Data analytics is the process of extracting, analyzing, and transforming large and varied raw data sets to uncover hidden pattterns, find useful information (such as market trends and customer preferences), and eventually draw conclusion that can help business with organizational decision making.
4 Types of Data Analysis
According to Investopedia, Data Analytics is a broad term that encompasses many diverse types of data analysis. Below, 4 types of data analytics will be concisely explained, with the later being the more complex method, thus the one that bring more value.
1. Descriptive Analysis
As its name suggests, descriptive analysis describes in detail about an event or data that has happened over a period of time. The summary of the raw data will then be converted into a form that is easy to understand. This type of analysis is helpful in deriving patterns or drawing interpretations from past events, providing better strategies for the future.
In business, descriptive analysis is commonly used to determine the Key Performance Indicator (KPI) and evaluate the performance of the business. This is also the most frequently used type of analytics among organizations.
2. Diagnostic Analysis
Diagnostic Analysis focuses on diagnosing you data to find various problems that might be responsible for the poor performance of the company. This method allows analyst to dig deeper into an issue and make hypothesis on why something had happened. Diagnostic analysis is often associated with and can help analyst to deeply understand descriptive analysis.
Business implement this analytical method to minimize their loss and optimize their performance.
3. Predictive Analysis
While descriptive analysis is all about the past, predictive analysis deals with events in the near future. Just like the name, this method predicts the possibility of future events: what is likely going to happen, as well as estimating the accurate time of the occurrence based on the historical data and current events.
However, despite the benefits that predictive analysis could give to your company, the result and accuracy a forecasting bring are merely estimation, hence the need of a more careful treatment.
4. Prescriptive Analysis
The purpose of prescriptive analysis is to prescribe or to suggest a series of action to eliminate a future problem or take advantage of a promising trend. Prescriptive analysis combines the insights obtained from all the other analytical techniques. Uses the details provided by the descriptive and predictive analytics to give detailed suggestion for a careful decision making.
Prescriptive analysis is a complex field and requires more than historical data. But when combined with advanced tools and technologies, such as machine learning and algorithms, it can provide you with mature scenario for your business.
Inside the Process of Data Analytics
Why You Should Implement Data Analytics for Your Company
Implementing data analytics can help your business to optimize its performance. It can also help to improve operational efficiency, and reduce cost thus increase revenue of your company by identifying more efficient ways of doing business and responding more quickly to market trends.
A 2013 Workforce Performance Report released by Evolv state that:
“Fortune 500 companies are investing tens of billions of dollars in Big Data initiatives for a simple reason: to save money and gain market share by accurately predicting future behavior rather than guessing at it. Rapidly advancing analytics-based solutions have changed how forward-thinking employers manage their workforces to help them become more productive and profitable.”
Without ignoring the protection of the data, data analytics helps you to:
1. Continually Improve Performance
Internally, data improves individual and team performance and productivity by showing management areas of improvement and helping employees to be more aware of their work habits and activities. Compared to training or seminars, which is a one-time investment, this kind of data will be an on-going investment for your company. More and better opportunities of improvement will come up as more and better data is collected.
2. Enable Smarter Hiring Choices
Hiring new talents for your company is also one business activity where data-based decision-making is required. From talent shortages to skill gaps, there are a lot of reasons that make the hiring stage challenging. Especially in industries where many high-skilled talents are hunted, such as technology and healthcare industry.
Data analytics algorithms can be used to predict the engagements of potential employees which which will help your HR in making hiring decisions and choosing the right management method that can produce the most optimum performance. After the recruitment process is done, data analysis can also contribute in giving insight about the most effective training methods for certain target.
3. Have Competitive Advantage
In today’s competitive landscape, getting new customer is important, but keeping their loyalty is even more crucial. By sharing their data with your business, customer expect you to understand them. And through data analytics, you will be able to not only know their behaviours and needs, but also use the data to provide a relevant, seamless experience with the goal to develop a good, long-term relationship with your customers.
Aside from customer-related advantage, data analysis can also give you advantages in term of product development. Demand changes and technology advanced in a pace that company must constantly innovate to be able to catch up. Data analytics can help you to stay in the competition, as well as facilitate you to anticipate the market demands and develop the products before it requested.
4. Gain Revenue
As mentioned above, data analytics can help your business to optimize it performance and reduce operational cost, that will eventually lead to revenue gain. Implementation of data analytics in designing and controlling your operational process, and optimizing your business, can ensure efficiency and effectiveness that can fulfill customer’s expectation and achieve operational excellence.
Advanced analytical techniques can also help your business to improve field operations, efficiency, productivity, as well as optimize your company’s workforce to match the business needs and customer demands.
5. Make Well-Informed Decision Making
The core benefit of data analytics is to get enough information for decision-making purpose. Reports obtained from a well-performed data analytics can back up decisions with hard evidence and provide balance in situations where opinions vary widely.
By bringing together data from across the business, your company can get real-time insights into various business process such as finance, sales, marketing, and product development; view it in context, and make smarter business decisions to make improvement in products and services.
For reasons above, data analytics has been gaining hype and is in full swing since the past five years. One study about Fortune 100 companies from University of Texas revealed that making even very small improvements to existing data can reap big benefits.
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