Big Data Management: Challenges and Solutions

Managing Big Data And The Challenges Faced While Doing So

WHAT IS BIG DATA?

Big data is the term used to describe the enormous and rapidly expanding volume of data that frequently exists within an organization in a variety of forms and originates from several sources. Stated differently, it is vast, diverse, and dispersed. Big data has a huge impact on how firms in almost every industry make decisions, create products, and manage their operations. Big data's primary obstacles are related to organizational, technological, and operational limitations such as a lack of infrastructure or skilled personnel. Let us deconstruct these obstacles into manageable, easily understood problems and provide concrete solutions.

BIG DATA PROBLEMS AND THEIR SOLUTIONS

1. EVER GROWING VOLUME:

CHALLENGE: Big data truly embodies its name. Businesses are sitting on terabytes, if not exabytes, of data that is constantly expanding and may quickly become out of control if improperly handled. Businesses miss out on the chance to get value out of their data assets because they are unable to keep up with this expansion in the absence of sufficient design, processing capacity, and infrastructure.

SOLUTIONS: Utilize storage and management technology to handle the growing volume and difficulties associated with big data management. Make sure your decision aligns with your organizational requirements and business objectives, whether you go with cloud, on-premises hosting, or a hybrid strategy. Build tools and a scalable architecture that can adapt to the increasing amount of data without sacrificing its integrity.

2. POOR QUALITY DATA:

CHALLENGE:  One of the major problems with big data, which costs the US alone more than $3 trillion a year, is poor quality. So, what precisely is faulty data? Inconsistent, obsolete, missing, erroneous, illegible, and duplicate data might lower the whole set's quality. Serious big data issues can arise from even little mistakes and inconsistencies. For this reason, monitoring its quality is crucial. If not, there can be more harm than good. Errors, inefficiencies, and misleading insights are caused by poor data quality, and they ultimately result in costs to the organization.

SOLUTIONS: Establishing internal method and personnel to handle data is the first step toward excellent data hygiene. Adequate data governance should be established, deciding on the instruments and protocols for access control and data management. Utilize the many available current data management technologies to set up an efficient procedure for cleaning, filtering, sorting, enriching, and managing data in various ways.

3. MULTIPLE SOURCES OF DATA AND DIFFICULTY IN INTEGRATION:

CHALLENGE:  Obviously, more data is better. Well, until you know how to compile information for collaborative analysis, more data frequently doesn't translate into greater value. In actuality, finding or creating touch points that lead to insights and integrating heterogeneous data are two of the most difficult problems big data initiatives face.

SOLUTIONS: Make an inventory to determine where your data is coming from and whether integrating it for collaborative analysis makes sense. Use data integration technologies to link data from several sources, including databases, files, apps, and data warehouses, and get it ready for big data analysis. You may utilize products like Precisely or Qlik, which are specialist data integration solutions, or you can use Microsoft, SAP, Oracle, or other technologies that your company currently uses.

4. INCREASED COSTS OF PROJECTS AND INFRASTRUCTURE:

CHALLENGE:  One of the major obstacles preventing executives from monetizing their data, according to 50% of US executives and 39% of executives in Europe, is a constrained IT budget. The cost of implementing big data is high. It includes considerable initial investments that could not pay off right away, so cautious preparation is necessary. Furthermore, the infrastructure expands rapidly along with the volume of data. It might become all too simple to lose track of your possessions and the expense of maintaining them at some time.

SOLUTIONS: By regularly monitoring your infrastructure, big data may help you address the majority of the increasing cost problems. As you're building your data processing pipeline, start thinking about expenses early. Choose affordable instruments that meet your financial constraints.  Good DevOps and DataOps methods help balance scalability costs, find cost-saving possibilities, and monitor the services and resources you use for data management and storage.

5. SLOW TIME TO INSIGHT:

CHALLENGE:  The term "time to insight" describes how soon you may draw conclusions from your data before it becomes dated and unusable. One of the problems with large data that arises from inefficient data management techniques and laborious data pipelines is the slow time to insight. In certain business scenarios, this metric is more important than others.

SOLUTIONS: When working on IoT and big data projects, where automation and remote control depend heavily on low latency, you should think about utilizing edge and fog technologies to deliver analytics as near to the action as feasible. Fast response to real-time data will be possible, and the time to insight will be reduced.

You shouldn't stick to a rigid data approach. When creating and constructing your data pipeline, take an agile approach and do periodic reviews to identify inefficiencies and slowdowns. To provide and disseminate insights more quickly, make use of big data visualization tools and methodologies as well as contemporary artificial intelligence technology.