Part of being successful in business is making use of all the resources at your disposal. One of the most important of these resources is data. As businesses have made the transition to digital platforms, the access, amount, and capabilities of data have skyrocketed. But data alone cannot help your business; rather, data requires people to analyse it in order to draw insights and ultimately guide business decisions. This process is known as data analytics, and in recent times, the process has become largely automated that allowed to reduce the probability of human error and boost efficiency. But what exactly are the benefits of utilising data to guide business strategy? In this blog, we will address the questions above, along with a series of steps you can take to approach data analytics.
Our brief overview of data analytics above does not encompass its full definition. Data analytics is not subject to a particular sort of data, and it can be used on a wide array of information. The process is not the same for every company, but in short, data analytics can be used to uncover trends, insights, and a few other metrics in a large body of information. These findings can then be used to enhance pre-existing services or products within your business, forecast economic shifts in the market, and much more.
Benefits of data analytics
1. Optimise business operations
- By selectively gathering and analysing data, companies can improve their supply chain management by detecting production delays or where points of congestion originate from in order to prevent similar events from reoccurring.
2. Diminish risk
- Conducting business is an inherently risky task, but through data analytics, companies can predict potential downfalls and pursue preventative measures.
3. Improve resilience
- On the off chance that your company does undergo a setback, data analytics can be used to efficiently make suggestions in order to guide operations back on track. This benefit is especially amplified for companies with automated analytics software, which use AI to make real-time recommendations on how to resolve issues that arise.
4. Boost customer satisfaction rate
- With data analytics, comprehensive customer profiles can be created through collection of customer information from social media, surveys, e-commerce, and more. Businesses can then use these unique profiles to provide customers with a more customised experience, for example, by presenting product recommendations or generating targeted social media campaigns.
How to prepare a data analytics strategy?
As mentioned before, the data analytics process varies from company to company. But nevertheless, here are a few steps you can take to approach this:
Determine the way in which you would like your data to be organised. If your company has clients located throughout the world, then location would likely be one of these organisational factors. Or, if there is a wide age disparity amongst your clients, age would be another factor.
Collect the data. There are numerous ways in which you can do this, but to name a few: surveys, transactional history, social media monitoring, and registration data.
Organise the data. Use the grouping categories you created in step 1 to separate the data you have collected into distinct locations. This can be done on a variety of software types and applications.
Check the integrity of your data. During step 3, portions of your data may have been duplicated, deleted, or altered in some way. Maintaining the integrity of your data by checking for errors is an essential step in the data analytics process.
Types of data analytics
Once readied, this data should be sent to a professional analyst with a particular goal in mind. An analyst can then pursue a number of strategies to analyse such data, each with a different objective. The first is descriptive analytics, which as the name suggests, merely seeks to describe the data. Diagnostic analytics takes it a step further and seeks to understand the causes behind why certain aspects are the way they are. Predictive analytics attempts to predict future fluctuations in data based on current ones. Finally, prescriptive analytics uses predictive analytics to actually propose a specific course of action. If you want to find out more about different types of analytics and what are they used for, read our blog Types of Data Analytics.
Conclusion
Ultimately, data analytics is an extremely powerful tool that has several practical uses in the business world. From its ability to boost customer satisfaction rates, to its ability to mitigate risk, data analytics is a field your company should definitely invest in. Another application of data analytics that was not mentioned in this article, but that you should be aware of is data monetisation, which essentially means using data to produce economic benefits for your company. For a more expanded overview on this topic, please look at our Data Monetization blog.
About the author: Mark Roychowdhury is a Copywriter Intern at ei² niche consulting for #data #insights #performance www.eisquare.co.uk