I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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How can Big Data Analytics influence Decision Making?

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How can Big Data Analytics influence Decision Making?

By shiwaneeg |Email | May 3, 2018 | 35490 Views

Big data can transform how decision-makers view business problems and inform strategic decisions, allowing them to rely upon objective data. Good data, sound analysis and valuable insights are critical for mitigating risks, making balanced strategic decisions and competing against others.

Big data analytics is transforming corporate decision-making, pushing organizations to become more agile and responsive. We can expect a massive business transformation if we can harness the power of big data analytics to make key strategic decisions. Big data analytics provide real-time insights, which need to be actioned quickly to gain better value. Data analytics has become the key to corporate competitive advantage. 

Organizations that employ swift decision-making processes are able to realize better value from real-time data analytics and build a strong competitive advantage. It is paramount for businesses to understand the big data concept and how it impacts the organization activities. Below mentioned are ways in which Big Data facilitates faster and better decision making;
1. Accelerating Time-to-Answer

The time cycle for decision making is decreasing rapidly. Companies have to make decisions more quickly in this period than in the past. Accelerating decision-making time is crucial for the success of any organization. The use of Big Data doesn't change the urgency of decision making. Big Data analytics mitigates.

Customer reaction to a product is an important factor to consider when making a decision. Using data resources to understand the preferences of customers is one way of pointing out gaps existing in the market. However, the problem is how do you integrate and act in real time? The key is to know how to combine Big Data with your traditional Business Intelligence to create a more convenient data ecosystem that allows for the generation of new insights while executing your present plans.

Accelerating your time-to-answer is crucial for customer satisfaction. For example, if your answer time is usually in minutes, Big Data can reduce it to seconds. If it takes weeks for a client to have their problem tackled, then reducing it to days is more convenient for your customers. Customer retention is critical to the success of your organization.

2. Big Data and Traditional Business Intelligence Alignment

  • Integrate Big Data with Traditional BI

To reap maximum benefits of an accelerated TTA, a clearly defined relationship between Big Data and traditional BI should be established. The realization of good governance of Big Data, Traditional BI, and Organization is an important aspect of a successful organization. The two variants; Big Data and traditional analytics must work together for maximum results of Big Data application.
  • Break Company Boundaries

Another important consideration is that of organization boundaries and integrating IT and the business units. Organizations need to give out their limitations, think outside the box, cross boundaries so as to benefit from Big Data analytics.

The existence of data management and data analysis in different parts of an organization is one barrier that will need crossing. IT specialists are usually involved in data control and the analytics done separately. Big Data should be an Integral part of data management. Tearing down boundaries is crucial to an organization that aims in creating organizational initiatives that will accommodate Big Data. When Data control and management are integrated, then the response time is facilitated. The organization will have better real-time reactions, and it is able to build competitive advantages with improved performance.

Big Data organization alignment makes data useful without the need of perfection. When your data is in one place and organized in a way that suits your goals and mission as an organization, then you are in a better place than when your data is in different locations. The time it takes to gather the data into useful information is longer. Instead of taking weeks to get a favorable reaction it would take you hours to have similar results
3. Create a Big Data Ecosystem

The value of data is having, and giving the right answers. Asking the right questions is paramount for an appropriate solution. There is the need to discover and explain hypothesis. Afterward, a model is constructed to solve the issue at hand, and the solutions are applied.

A data ecosystem is the organization of data from all sources. The data is then used to prospect for new challenges that may arise. In traditional BI you have to analyze one piece of data to the next. There is a need to integrate data for a meaningful result. Data collection and cleansing is hectic and time consuming such that by the time you are done and have the answer the new client has already left or the old one has resulted to faster means.

Big Data analytics allow your organization to do the data collection, integration, create a model to test the hypothesis and metrics that counter-check the applications of the solutions in the real-time. The process gives an accurate yet speedy answer. Thus, when a customer forwards a question they get faster answers.

Big Data analytics is everywhere from marketing to demand and supply units of an organization. In the supply chains, Big Data helps organizations to predict the future. The organization can then change the plans such that the results are altered, and success achieved. This is the aspect that movie makers and musicians apply when bringing their product in the market. The producers check the market, source data from the systems, understand what the consumers want, create a model and metrics to test the solutions, and apply the results in real time.

Source: HOB