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Businesses of all sizes are now leveraging big data across a multitude of business processes. This allows them to better guide and manage their companies. Read on to learn how you, too, can make better business decisions by exploiting big data.
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Making the right decisions at the right time is at the heart of operating a successful business. The more accurate a business leader can be with their decision-making processes, the more profit the company will make. Naturally, and the more successful it will become as well (in theory, at least).
Fortunately, we are in the midst of the digital age. And thanks to rapid advancements in technology, C-suite executives now have the unprecedented ability to incorporate data and statistics into their operations. As a result, companies are now leveraging the world of big data and analytics across a multitude of business processes. This gives them the ability to simultaneously refine, optimize, and eliminate much of the guesswork that previously depended on intuition.
The Challenges of Leveraging Big Data
There’s no denying that organizations that can effectively utilize data to obtain insights often remain ahead of the competition. Moreover, they can also better anticipate and their customers’ expectations. This is because they are better situated to predict consumer wants and demands. This means they can more promptly bring new products to market to meet this demand. By utilizing the power of big data, companies can also quickly innovate. They can develop new income sources, reduce costs, and increase the efficiency of their workforce.
With that said, shifting to become a data-driven company is not as simple as it sounds. Whether you’re attempting to utilize data to improve decision-making related to customers, finance, internal operations, or your workforce, often, it involves a significant investment in technology and software. Moreover, it necessitates a significant cultural shift in which employees are willing to accept data as the single point of truth.
On that note, here are some of the main challenges organizations face when leveraging big data to make better business decisions:
Dealing with an Overwhelming Amount of Data
Believe it or not, we are currently on course to produce more than 74 zettabytes of data by the end of 2021. By 2025 global data creation is projected to grow to more than 180 zettabytes, a simply staggering sum. In other words, the problem for businesses isn’t where to find more data. It’s figuring out what data is relevant, where to store it, and how to put it to good use.
For these reasons, old-school legacy systems are quickly becoming obsolete. They simply cannot keep up with the current demands brought about by the copious amounts of data businesses collect these days. As a result, on-premises data storage systems are being replaced by cloud solutions such as cloud data warehouses and data lakes. In short, business leaders are discovering that solutions such as these are essential to leveraging big data.
Handling Multiple Data Formats
One of the significant challenges of leveraging big data is figuring out a way to store, process, and analyze data in its different formats. After all, data comes from a wide variety of sources. You can’t always guarantee that it will arrive at your data warehouse the way you would like it. For example, you could have software that outputs data in an XML file, but you need to process it using an API that requires JSON. This can cause a headache for your data analysts if you do not have the proper infrastructure to handle these events.
Rather than converting files every time you need to store or access them, it’s a good idea to opt for a data warehousing solution. These solutions can support various file types to make things go a bit smoother. It’s worth checking out comparisons between some of the leading data warehousing providers. This will help you to figure out which one best suits your needs.
Let’s take Databricks vs. Snowflake as an example. Snowflake enables users to upload and save both structured and semi-structured files without the need for an ETL (Extract, Transform, Load) solution. An ETL solution allows a business to organize data before loading it into a data warehouse. However, it does require that you structure all of your data before you can load it and work with it. Databricks, on the other hand, works with all data types in their native format. This can make Databricks a better fit than Snowflake for data science and machine learning workloads. Of course, this depends on your company’s specific needs.
Pooling Data Silos to Better Leverage Big Data
Data silos may seem harmless to your business. However, they create barriers to information sharing and cross-department collaboration. This is not ideal since it can (and will) result in widespread inefficiencies across the organization. Moreover, data quality may suffer.
In order to leverage the power of big data effectively, businesses need to find a way to centralize data. They do this by pooling all corporate data into a cloud-based data warehouse or data lake. A data lake or data warehouse acts as a central data repository that is optimized for efficient analysis. Data from disparate sources can then be homogenized, consolidated, and integrated. Thereafter, managers can grant access to the individuals and groups that require it.
Capturing Actionable Insights
Here’s one of the simplest yet most essential truths about leveraging big data in business: Data is only as valuable as the insights you capture from it. There’s no use collecting large troves of data for it to sit there unanalyzed. In this state, it does not contribute toward your business objectives.
To put it another way, data is an important resource that must be properly exploited. The only way to do so is to have a clear strategy in place from the get-go. What are the key drivers of income, expenditure, and risk in the industry you’re engaged in? Once you’ve answered this question, you can start using analytics to maximize the effect and return on your data investment.
Transforming Company Culture
As we alluded to earlier, company culture can potentially be one of the biggest obstacles to effectively leveraging big data within your company. If you want to implement data into your decision-making processes, then the rest of your workforce must buy into this new way of doing things. For many employees, switching to a data-driven approach may mean that they have to change how they approach their job entirely. This can be a difficult transition.
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To make the switch to data systems go as smoothly as possible, business leaders must prioritize communication at all levels of the organization. They must ensure that all employees understand how and why their job role will be affected, as well as the benefits the company expects to receive as a result of the change.
Second, it’s crucial to establish realistic expectations. Data is a valuable asset that flows throughout an organization, making effective data management a difficult task. As a result, delivering successful data outcomes will be impossible until businesses set achievable goals for incorporating data into their corporate decision-making.
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