Data Analytics as a Decision-Making Tool in Business

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Let’s find out how various types of data analytics can be helpful in business decision making with features, benefits and persistent challenges!

Data can be categorized as nominal, interval, ordinal, and ratio. The application of analytics in making business decisions goes by planning better. Data Analytics helps businesses in optimizing their performances by implementing various business models. Organizations that implement customer behaviour into their operations witness a rise in their overall profits by helping them in reducing the cost by identifying more efficient ways of doing business. (The profit percentages are subject to change). Decisions can be automated using algorithms and technologies. Customer data is invaluable for businesses who wish to improve their performance and leverage their insights. Let’s find out more.

(Please Note: The term ‘Data’ is often interchangeable with ‘Big Data’, and should be perceived as applicable)

Can data analytics support decision making?

Yes, putting analytics in place can allow businesses to plan better by providing an insight based on repetitive patterns. Organizations do combine AI in conjunction with automation and analytics, capturing customer behaviours to make decisions and have an edge above their competition.

What are the various types of data?

It is important to understand the various types of data that businesses collect before diving into the types of data analytics. Types of consumer data that businesses collect:

  • Personal Data: Customer’s data such as Social Security Numbers, identifiable and non-identifiable information including IP address, their device type, web browser cookies, device ID’s etc.
  • Engagement Data: The engagement data like how customers interact with a business website, mobile apps, social media apps, emails, paid advertisements, customer service routes etc.
  • Behavioural Data: The behavioural data including transactional details like purchase histories, product usage information, repeated actions, qualitative data like mouse movement information etc.
  • Attitudinal Data: The attitudinal data like metrics on consumer satisfaction, product desirability, purchase criteria and more.

How data analytics helps businesses in improving their decision making?

Businesses have to collect data for multiple reasons. This requires following more than one approach and technology. Businesses capture data, analyze it, and repurpose it to understand customer behaviour, day to day operations, making more informed business decisions, and learning about customers – their next moves, their preferences in specific. Like private companies, public companies also collect customer data to improve overall communication and form social media strategy for their business.

Monitoring data was perceived to understand consumer behaviour but it can also help in detecting any fraudulent activities (probably by competitors) to understand the business strategies, suspicious transactions and flag such accounts early on. This is how credit card companies get customer names with their phone numbers to annoy us in real.

What types of data analytics do companies choose?

Data Analytics can be classified into four types:

Descriptive Analytics

What does it tell: What is happening in a business?

How does it help? Effective visualization of comprehensive, accurate, and live data

Diagnostic Analytics

What does it tell: Why is it happening in this business?

How does it help? It helps us reach the basis of the problem and to isolate all confounding information

Predictive Analytics

What does it tell: What could happen to this business in future?

How does it help? It helps in automating decisions by making use of algorithms and technologies. It also illustrates business strategies, historical patterns used to predict specific outcomes by making use of certain algorithms

Prescriptive Analytics

What does it tell: What should we do about it?

How does it help? It helps in the application of analytical techniques to make specific recommendations. It also tells about the suggestive actions to be taken on basis of testing strategy outcomes.

The sequence of the decision-making process goes like (1) Descriptive Analytics < (2) Diagnostic Analytics < (3) Predictive Analytics < (4) Prescriptive Analytics

What steps are involved in data analysis?

Data Analysis is a five-step process that helps improve data analysis skills and simplify business decision making. It includes:

  1. Defining your questions
  2. Setting clear measurement priorities
  3. Data Collection
  4. Data Analysis
  5. Interpreting Results

(Please Note: This list is not comprehensive)

Which compliance rules should businesses follow while collecting data?

Businesses can revise and improve their policies and procedures by collecting customer data. Here is a list of compliance rules that must be adhered during data collection:

  • The owner of the data has the right to ensure that third-parties may or may not use their data and that privacy and legal issues with data governance are associated with ventures into the cloud.
  • Business platforms (B2B, B2C) are required to ensure that they follow appropriate procedures and sharing agreement so that personally identifiable information remains confidential and protected from unauthorized disclosure. To ensure this, businesses need to conduct regular data quality audits and enforce updated quality control and also put required corrective measures.
  • Data Governance rules and requirements must be aligned with policy priorities secured with consent from key stakeholders.
  • Businesses must also conduct an assessment to ensure the long-term sustainability of the proposed or established data governance policies and procedures that include adequate staffing, tools, resources, and technologies.
  • A business also needs to have standard policies and procedures about all aspects of data governance and the data management lifecycle including collection, maintenance, usage and dissemination (clearly defined and documented).
  • And importantly, policies and procedures must be established to ensure the continuity of data services in an event of a data breach, loss, or other disasters (this includes a disaster recovery plan)

Challenges with types of data analytics

Implementing data analytics requires a lot of research on the business purpose, the target audience, the customer’s preferences, and their purchase behaviour. The businesses need to analyze the data and interpret their results for the success of their policies and procedures. The problems with managing a large volume of data can often be out of reach, complicated and time-consuming.  Often the need for synchronization across disparate data sources, the paucity of funds and skilled professionals to efficiently manage the available scattered data resources, the shortage of people who understand big data analysis, the uncertainty of data management landscape, maintaining the quality of stored data and security and privacy of data can be challenging.

Conclusive: Were we able to prove the role of data analytics in solving the decision-making process in business?

We started by answering if data analytics can support business decision making? We hope that this illustration justifies this.

Overall, relying on data can help improve the effectiveness of any operation which can eventually make better business decisions and improve overall management. The effectiveness of data can improve management, affecting the business operations directly. As data technology improves, business professionals handling such data analytics tasks are deemed to increase.

We are up to discussing more about data analytics architecture and its role in business analytics, machine learning, visualization, artificial intelligence and more. If you find this useful or would like to know more about data analytics and how you can leverage it to its potential do connect with us!

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About Author
Vipin Jain

Vipin Jain

Vipin Jain is the Co-Founder and CEO at Konstant Infosolutions and is in charge of marketing, project management, administration and R&D at the company. With his marketing background, Vipin Jain has developed and honed the company’s vision, corporate structure & initiatives and its goals, and brought the company into the current era of success.

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