DATA ANALYTICS

Data plays a vital role in the digital age. However, many companies struggle to take full advantage of the data created. Only 0.5% of the world's data is processed and utilised each year.

According to a report from Wall Street Journal, large corporations like Amazon, Alphabet, and Microsoft have invested $32 billion in "data filterers" with predictions that data will grow faster and even more extensive in the near future. Therefore, Data Analytics becomes an essential part of today’s businesses.

Some of the applications of Data Analytics may include:

• Analytical forecasting and data analysis models to provide strategic suggestions.
• Assist managers in making decisions at critical times.
• Identify future needs, trends in behaviour and shopping habits of customers.

So, how exactly does Data Analytics affect modern business? Let’s have a closer look!

In the fast-changing IT environment, commodity data protection tools and basic archives are simply not enough as Archiving and Backup are the core of information governance’s future.

You have to understand what you are keeping, when to delete, and how to draw insights from your data. Data terms, such as Data Lake, Data Warehouse, Data democratisation, are essential to determine a successful business.

Besides, not only must we record the daily incoming data, we also need to make sense out of it. However, we do not have the capabilities nor resources to analyse such enormous volume of data; it is even challenging with the help of machines. This is where turning data into smart data or Data Management steps in and helps you to make decisions without any processing power.

Monitoring data and purchase orders play vital parts in the business operation. However, errors made by people who are not familiar with analysing data are often responsible for many inventory valuation issues. That’s the reason why managers need to pay close attention to the analysis of data to gain business insight.

Some organisations, especially retailers, have tried leveraging customer data. Many forward-thinking companies have taken a more significant step and invested in a Business Intelligence (BI) plan and gotten familiar with Networked Analytics in BI to enhance their power.

Nevertheless, to achieve maximum return on investments, you first need to understand the various definitions and terms of BI and Analytics (you can get to know them here: part 1, part 2, part 3, part 4) and the ABCs of Data-as-a-Service, which provides faster, cheaper, and easier access to critical data.

To improve data literacy, many companies even appoint a Chief Data Officer (CDO) as they gradually realise how CDOs can help transform their enterprises. Besides data governance, another important task for managers is data protection. How to keep your internal data safe in the workplace? Or make sure it is confidential when your employees leave? Once these questions are answered, the data security level of the whole organisation can be improved dramatically.

Big Data is a term used to refer to a substantially massive set of data which is so complex that traditional data processing tools and applications cannot handle it. Instead, these data must be collected, stored, shared differently. Big Data contains a great deal of valuable information that, if extracted successfully with suitable tools, will benefit companies, especially those who operate in Manufacturing in identifying new opportunities, expanding into new markets, making plans on a customer base.

Apart from manufacturing businesses, the hospitality and real estate industries can also take advantage of Data Analytics through hotel revenue management systems and OLAP technology. With this assistant, hoteliers are able to easily compare prices (to competitors), maintain the whole system, and access to smart data presentations, therefore, enhance the overall operational performance. In the meantime, Data Analytics helps to create more value and online visibility, make better decision and encourage individual customers, which boosts the efficiency for real estate enterprises.

To Chief Finance Officers (CFOs), it is better to have full control of the financial data in corporate reports, so they would love an assessing data system that can both analyse and protect the crucial information. Many vendors have integrated the security feature into their products to keep the competitive advantages (like Infor Sunsytems Cloud). Not to stop there, even the non-financial data should not be overlooked as it has certain effects on the overall result.

As one of the leading ride-sharing services in Southeast Asia, Grab has adopted Data Analytics solutions to make more informed, data-driven decisions. The ability to handle a vast amount of data quickly has allowed Grab product teams to track multiple metrics in real-time and better understand their customers.

Tesla Motors is an American company specialising in the design, manufacture and distribution of automotive electrical cars and components for electric vehicles. With the rapid growth and aggressive goals, Tesla has turned to advanced Data Analytics software to gain greater information governance to catch up with the needs of expanding.

CheapCaribbean - a company providing travel and holiday resort services, especially in Mexico and the Caribbean, used to heavily rely on time-consuming and not effective Excel for data analysis. They adopted the modern networked analytics architecture to taking the business to the next level.

In the Citrix case study, we have a chance to know how this multinational software company has solved its data analytics problem and architected its digital supply chain. They invested in a Cloud BI system that can aggregate and analyse data from 400 data sources in a single view, go live in fewer than 90 days, and help achieve a five-fold increase in inventory turns.

By using a Cloud BI platform to combine and analyse the customer-related data, Build.com -  an online home improvement retailer, has been able to boost revenue and gain greater customer insights. They can extend their customer lifetime value, reduce customer acquisition costs, and focus more on profitable customers for better overall results.

If your organisation needs to convert raw data to trusted analytics, you should think of Birst - one of the pioneers in the Cloud revolution. Their Connected Data Prep empowers businesses to access and prepare data with a user-friendly, visual experience.

Another trustful Data Analytics vendor is Tableau. Specifically, Tableau 10.5 delivers an extraordinary speed at analysing data, cementing the Tableau’s position as a pioneer in data visualisation and analytics.

You can also consider choosing Infor for your data requirements. The Infor Data Lake unites all of your data on CloudSuite, Internet of Things, documents, third-party application data and more into just one repository. Infor Data Lake allows you to utilise your data sources to the fullest.

Of course, there are many other reputable Data Analytics vendors available in the market. We offer you a list of Analytics & Business Intelligence Vendors in 2018. Simply click here to find out!