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Data holds the key to making better and profitable business decisions

April 21, 2022
Read Time 2 mins

Today’s largest and most successful organisations use data to their advantage when making high-impact decisions.

In business and in life we are generating ever-increasing volumes of data. One of the most valuable uses for it is helping businesses to make better decisions. When a business uses data effectively to make decisions, it can lead to increased sales, improved business agility, better customer service and engagement, and empowered internal decision-makers.

The benefits of data-driven decisions

  1. More confident decisions
    Data-driven decision-making is the process of making organisational decisions based on actual data, rather than intuition or observation. This helps to instil trust in committing to a decision.

    "It is easier to reach a confident decision about virtually any business challenge, whether you’re deciding to launch or discontinue a product, head into a new market, or re-jig your content strategy when it is based on the facts." Jamie Scott, Chief Technology Officer, Tarsus Distribution

Because data benchmarks a current situation, it allows decision-makers to better understand the impact that any decision they make will have on the business.

  1. A more proactive future
    At first, data-driven decision-making will require an organisation to react and respond to the existing data, a process which is valuable in itself.

Over time, however, through a combination of practices and correct data gathering processes, the business can begin to proactively identify new business opportunities before the competition does or detect market threats in advance.

  1. Cost savings

One of the most successful outcomes of data-driven decision-making is decreased expenses. Using data to inform decisions improves operational efficiency, enabling cost-cutting across business areas.

To effectively utilise data, experts do the following:

  1. Know the objectives
    Determine what the problems are in a given industry and understand them thoroughly. It is also important to identify the business questions that require answers as these will inform the organisational strategy.

  2. Identify data sources
    Data may be collected from many different sources – disparate databases, web-driven feedback forms, and even social media. It is vital to develop a strategy to find common variables and present the data in a way that’s accessible to users in other business scenarios too.

  3. Clean the data
    It is said that 80% of a data analyst’s time is spent cleaning and organising data, and only 20% is used to perform analyses. This illustrates the importance of having clean, orderly information before anyone can attempt to interpret what it might mean for the organisation.

    Data cleaning involves preparing raw data for analysis by removing or correcting data that is incorrect, incomplete, or irrelevant. It requires data to be organised and catalogued so that it can be translated and understood.

  4. Perform statistical analysis
    Building models allows users to test the data and answer the business questions identified earlier in the process. Testing different models such as linear regressions, decision trees, and others will help determine which method is best suited to a particular data set.

Effective data management and storage is fundamental to data-driven decision-making. This helps to optimise the use of data by people, the business and connected devices. A robust data management strategy is becoming more important than ever as organisations increasingly rely on intangible assets to create value.

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