Leveraging the latest technologies, businesses have access to granular data regarding their operations and customers. For Big Data companies, relevant data is a key asset. Nevertheless, understanding what to do with such large volumes of data can be difficult and costly for businesses. Even with the right set of tools and software at disposal, data analysis can be tough.
With so much complexity around Big Data services, there are high chances of businesses running into pitfalls with their datasets. In this blog, we will uncover the top 11 Big Data mistakes businesses should avoid in 2022.
Without further ado, let’s get started!
Businesses generally start their Big Data journeys with large amounts of data without realizing how it will help them. Such raw data does not yield any value. Apart from utilizing time and resources, such data leaves the projects stagnant and breaks down the analysis.
A strong piece of advice to avoid this ill practice is to create a Big Data roadmap focusing on critical short and long-term business decisions that will help everyone understand the value that data or information will bring to the business. It is ideal for the organization to start with a well-defined strategy. The data should support or deny the business’s hypotheses but should not be neutral.
Not having a central oversight to collect data generally leads businesses to end up collecting duplicate data. In such a case, the columns being used incorrectly lead to poor quality of data. Hence, every business must have a committee with the privileges to mandate the data and offer proper training to the users.
Businesses collect and store data without doing anything productive with it. Such unused data does not offer valuable insights. Instead of letting the data sit inside a silo, businesses should start using it to retrieve valuable insights to improve business operations, develop strategic product roadmap and address complex challenges.
Hadoop can be implemented as the solution to this issue. A Hadoop data lake is a dynamic data management platform with multiple Hadoop clusters. It is used to process and store non-relational data including log files, social media posts, JSON objects, images, sensor data, and more.
To effectively manage the business assets of customer data, businesses leverage technology to simplify data collection as data volumes vary. Modern businesses are enabling core operations like Artificial Intelligence and Blockchain for customization.
Instead of focusing on long-term value and benefits, businesses lookout for short-term ROI from these technologies. Focusing on the short-term ROI is a common mistake and should be avoided.
Huge volumes of data and a lack of trained professionals is an issues businesses face. Businesses cannot retrieve valuable insights without knowing how to analyze Big Data. This happens due to a lack of experts. The predictive maintenance tools like the sensors do not analyze the data.
Businesses require experts who know how to properly analyze the data to fetch valuable insights that can help in organizational change and scaling.
With giants like Amazon offering management services and cloud storage solutions, businesses lose a lot if they do not move their data from on-premise to the Cloud. Additionally, Cloud offers the flexibility to use multiple servers where businesses can effectively analyze the spikes in numbers and prepare the flow of everyday tasks.
Most businesses are subject to market changes and should not try to hold on to old and outdated Key Performance Indicators (KPIs) used to measure success in the past. Rather than focusing on outdated approaches and KPIs, businesses should focus more on the latest technologies to build advanced big data analytics tools that reflect the current KPIs. Staying updated with the latest trends helps identify the current performance indicators to scale the market.
Businesses want the highest ROI but they do not use the data to scale over time. There should be employee training sessions to follow not the best but the latest practices and focus on organizing the data to see the patterns. This approach helps the employees to make and implement functional improvements.
Businesses sometimes use Big Data solutions even when their datasets are small. Even though all businesses should use data analysis to make decisions, most issues do not require complex tools associated with Big Data. Even though all businesses should use data analysis to make their decisions, numerous problems do not require expensive tools.
On top of that, before investing in time, money or expensive tools for Big Data analysis, businesses should check with the Machine Learning experts to understand if the data pursued is likely to improve results and will help in preparing business strategies. Professionals should also check the integrity of the datasets.
After collecting data, several businesses find it difficult to get valuable insights because of not investing enough resources to set up a dedicated Business intelligence team. A dedicated BI team helps the organization collect the Big data, analyze it thoroughly and share the relevant information that can drive business.
While working with Big Data, businesses should ensure that proper data security and governance are implemented to mitigate the security risks around it. To achieve data security, businesses should follow an adroit approach and a unified system of controls and processes. This approach helps the organization have control of privileged users and audit data manipulations.
Big Data helps businesses create new growth opportunities and streamline core operations. It helps in preparing an effective strategy by combining the industry data including services, solutions, customer preferences, and products.
DRC Systems is on a mission to fast-track businesses into the digital era. We are driving digital transformation by leveraging our expertise in deep analytics and Big Data. Contact us today and talk to our Big Data experts and start your data journey with strategic business moves.
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