Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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Analyzing Data With Good Actionable Business Intelligence

By Jyoti Nigania |Email | Dec 20, 2018 | 4938 Views

However, for the company to be successful, this area must be integrated within the company, and for this to succeed it is necessary first to define the policies and culture of a business.

Another challenge develops when businesses gather data and organize it without considering the questions they will be used when processing the data. Each step in the data collection process should lead toward gathering useful data and analyzing it with the goal of developing actionable Business Intelligence.

The sheer volume of incoming data could easily overwhelm a novice Data Manager. It has been estimated that 2.5 quintillion bytes of data are created each day. Data Managers must face the challenge of collecting, managing and finding value in the data.

Taking a reactive approach rather than a proactive approach to problems that arise is considered one of the most serious problems facing Data Managers. Many don't realize there is a problem with the data, until after the damage has been done. A solid, proactive maintenance program can save significant amounts of money and prevent the staff from suffering downtime.

Best Practices in Data Management:
Gaining the insights needed to make data-driven decisions begins with asking a business question and then collecting the data needed to answer the question. This requires collecting vast amounts of data from different sources and then using best practices while storing and organizing it, cleaning and mining it, and then analyzing and presenting it in the best way to make business decisions. The use of best practices produces better analytics. As much as 40 percent of all strategic processes break down as a result of poor data. A few useful best practices Data Managers should use include:

Determine what data can best predict an outcome with the understanding more data is better than less. Using data templates can ensure only relevant, usable data will be collected.
Scrub data, which requires profiling, adding missing data values, correcting data, and finding duplicate data.
Introduce Data Governance practices to help ensure data is high quality. A well-designed Data Governance program includes clearly defined procedures, long-term-planning, and a governing council.
Documented data should describe its content, context, parameters, and identify staff members who can use the data. The documentation process should also include creating broad-ranging metadata tags to promote the discovery and use of data.

Data Management Platforms:
The top Data Management platforms provide businesses with the ability to leverage Big Data from multiple sources in real-time. Using a good platform allows a Data Manager to be more effective with both staff and customers. The top Data Management platforms provide organizations and businesses with a broad view of their customers and critical insights into their behavior. Data Management platforms have helped organizations collect, sort, and store their information, allowing them to repackage it in ways that are useful to managers and sales staff. Data Management platforms, combined with Data Analytics, allow businesses to:
  • Personalize the customer's experience
  • Improve customer engagement
  • Identify the causes of marketing failures in real-time
  • Increase customer loyalty
  • Acquire revenues linked with data-driven marketing
A state-of-the-art data platform should be able to transparently automate and organize the lifecycle of data. By optimizing hardware utilization and the data's lifecycle, costs can be reduced through the coordination of security, tiering, and redundancy. A data platform acts as a software layer to control underlying storage resources.

How is Data Managed?
The use of Master Data files is a popular method for managing data. This is called Master Data Management (MDM). MDM files define assets and properties with the intention of removing vague or conflicting data policies and give an organization near-total control over its data. Effective Data Management can reduce errors by using the MDM as the accurate master copy for the organization's most important information. This helps ensure any applications built using master data are accurate and effective.

However, managing data efficiently requires more than MDM. The organization of data needs to line up with the organization's business strategy and what data the company needs to move forward. The challenge most Data Managers face is how to best use Analytics and how to integrate Analytics with business processes. Integrating Analytics with Data Management will assure a higher degree of success in Analytics projects. When archiving data, a business should use a storage system capable of supporting data discovery, access, and distribution, and when data archiving, regulations and policies must be considered.

Data is also subject to quality control, which might involve double-checking manually-entered data through the use of quality level flags designed to indicate potential problems and check format consistency. Additionally, data should be documented, defining its context, content, and parameters.

Data Management is an essential step toward controlling the massive amounts of structured and unstructured data deluging organizations every day. The use of best practices helps an organization maximize the value of their data and find business insights. An additional benefit is improved compliance the result of organizations striving for greater transparency in their business processes. Data Management can also enhance customer relationships and loyalty by tailoring services to customers and personalizing their interactions.

Source: HOB