Data is everywhere, and the amount created every day is simply astounding. However, all this information, if not properly managed, will lead to confusion. So, as a business, how will you navigate through the chaos to find clarity?
Confusion is the bane of any organisation—it breeds mistakes and interferes with progress and growth. Big Data analysis can be the key to your success, but it won’t magically display the path towards it. Simply put, Big Data has to be gathered and analysed the right way so that you can use it to make sense of chaos.
Organisation is Key
While it can serve as a great resource for insights, the sheer amount of data today’s organisations hold can also lead to complications and disadvantages. With so much data at hand, figuring out how to make use of it quickly becomes a challenge. What should you keep? What matters to your business? Which are necessary, which are useless? These are some of the questions you need to answer when dealing with your data.
Depending on the amount of data you have and the nature of your business, the answers may not come easy. However, you’re creating and collecting data because you know it has value, so you need to do something with it. Thus, if you want to maximise your data, you have to classify and organise it first—the associated parameters of this task should be related to the answers to the aforementioned questions. In other words, you should organise your data with the basic question, “What do I want to do and achieve with it?” in mind.
Integrating External Sources
Not all the data you’ll collect or need will be produced by your own organisation. Many times, third-party sources will produce the data that, when integrated to your own, will provide additional context, resulting in better understanding.
For example, in retail, data related to consumer purchase behaviour derived from internal sources such as polls, purchase history, and inventory listings is helpful. Adding relevant external data such as demographics and social media engagement could help to more effectively answer the “why” and “how” parts of the consumer purchase equation, allowing you to deliver better, more targeted marketing and distribution solutions.
Past Data Can Wreak New Havoc
Your historical data can be a goldmine of insights that will help you enhance sales forecasting strategies and better predict what your customers want, possibly even before they want it. Without proper analysis though, your data will just be sitting there, not giving you the necessary insights on what you should do next.
Worse, accumulating disorganised past data can lead to more confusion. Without proper context and analysis of data relationships between past and current data, chaos will unfold. Remember that past data may contradict current data. Not knowing and understanding the factors that lead to this will result in poor decision-making. Data, whether old or new, should be treated in a way that transforms it into actionable insights.
This is why Big Data analysis and management is important as it allows you to organise and classify your data. This allows you to view your data holistically while also letting you know which specific “chunks” or data sets are important (and which are useless), in relation to certain tasks and objectives.
From Chaos to Insight
Going back to the customer behaviour data example, businesses may have more data than they would ever need or use in terms of understanding customer journeys (which can be a confusing affair). Which kinds of data will be beneficial? How will you dig through all that data to find something useful?
In this scenario, you’ll need to prioritise and look at data related to areas such as customer interactions, purchase habits, marketing engagement, and even after-sales service. Doing so reveals where the roadblocks are in customer conversion and retention. When you have the journey mapped out and the data to support each section, it becomes easier to find the problems in the whole process and address them accordingly.
At what point are potential customers leaving? Why do their loyalty and buying patterns change? Is your service hurting customer retention? By finding the areas that need more support, you’ll be able to make the necessary improvements in customer retention and satisfaction. Ignoring data you don’t need and focusing on those you do streamlines the process, cuts through the chaos, and informs decision-making.
Pharmaceutical Firm Reduces Clinical Trial Simulations Costs through Data
American pharmaceutical company Bristol-Myers Squibb collects, retains, and analyses plenty of data from internal and external sources. From clinical trials to the components of drug-making, the amount of information produced is massive, and the company understands the breadth of the data available to them and how it could impact their operations.
Armed with Big Data and the benefits of cloud computing in managing, accessing, and storing information, Bristol-Myers Squibb is able to complete clinical trial simulations faster than ever before. Through tools and strategies that allow them to analyse and organise data, they’re able to avoid confusion and quickly wade through the data they collect. This has allowed the company to finish simulations 98% faster which, in turn, has helped them improve dosing level accuracy, making medications safer. Before Big Data collection and analysis, much of the process was manual and more complicated, which led to longer lead times.
What Will You Find?
The impact of Big Data is never in the data itself but rather in what you do with it. To get to the point where confusion turns to clarity, you need the right tool for effective and efficient data analysis and interpretation. Here’s where Latize’s intelligent data management platform Ulysses comes in. By harmonising disparate data from internal and external sources, it enables users to avoid chaos associated with having huge amounts of data and derive the necessary insights to improve operations and services. Click here to find out more about how Ulysses can help.
