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7 Best Practices of Big Data Management in the IT Sector | PromptCloud
 

7 Best Practices of Big Data Management in the IT Sector

7 Best Practices of Big Data Management in the IT Sector

With the massive digitization that we are witnessing today around the planet, businesses now have a lot more to account for when it comes to formulating strategies and making management decisions. The market has developed a number of new variables and mode of operations which were not even in existence just a couple of decades back. These new techniques and business enablers need to be effectively accounted for, if success in business is a core aspiration for your company.

Among the various concepts and entities that have emerged in the digital world over the last decade, it is the concept of Big Data which probably has the biggest business implication than anything else. Big Data technologies have enabled businesses to walk into uncharted territories, retrieve relevant and actionable information, and streamline their marketing efforts to a point where decisions can be made proactively with accurate forecasting of future events and trends for maximum success.

7 Best Practices for Big Data

For businesses dealing with Big Data, it is extremely important to work out the best method of Big Data management. With such a wide field and scope of data, it is paramount to have a strong foothold in foundational approaches, skills and processes. It is equally important to acknowledge that it is a rapidly evolving technology growing by leaps and bounds hence it is necessary to experiment and then determine which is the best fit technique suited exclusively for your company. This can be a great way to ensure that your Big Data management efforts bear fruit and you are able to leverage and harness this wonderful resource for achieving success in your business.

Big Data Management Basics

Businesses are waking up to the fact that more and more of the world has started leaving an influential digital footprint. With the advent of mobile devices, cloud technology and social platforms, a humongous amount of data is generated every day and added to the Big Data pool.  To make sense of this large mass of data, companies need to have a management strategy in place. With various data extraction technologies being available at hand, extracting the right data can be accomplished. The unstructured and structured data must then be correctly sorted and stored at the right place and in the right order for it to make sense later. Big Data management, when carried out effectively, can empower any business and allow the generation of information that can instrumental to its success. Tailoring the tool and technique specifically for your company allows you to have a clearly defined path towards the correct and efficient use of Big Data for furthering your business interests.

7 Best Practices

Thinkers, innovators and industry leaders who have reached a position of power and respect in their chosen markets have all formulated clear Big Data management practices for their businesses. Taking a cue from the practices that work and putting together some of the more effective practices that can truly empower your company, we end up with seven important points –

  1. Having a clearly defined business problem you want to solve with Big Data

    It is the first and foremost requirement for effective Big Data management in future. A lot of businesses have the view that Big Data is crucial for their success and spend time and resources trying to harness it without clear visibility of outcomes expected.This happens because before anything else, there is the need to identify a clear business problem and demarcate a business requirement which Big Data can effectively fulfil, and use that insight to start collecting and managing Big Data. This gives your Big Data management efforts some much needed initial direction which can then govern its use.

    Start by identifying a business problem or requirement that you are trying to satisfy via Big Data, and take it from there. The problem will depend on the particular business and its characteristic needs and nuances. Whether it is digital marketing, making hiring decisions or managing inventory more efficiently, clearly defining your scope prior to delving into Big Data management is an essential step for your efforts to succeed.

  1. Determining your business value and mission and connecting your Big Data management efforts to actionable insights

    This is another valuable step that you can take to ensure that your Big Data project bears fruit and helps you achieve success. After defining your business problem, it is time to also quantify and better understand the solution that Big Data can provide.Start by analyzing your Big Data project and trying to pinpoint the ROI that you are looking for. Establishing a concrete ROI expectation is tantamount to finding the correct justification for your Big Data project and helps in further fine-tuning its management in the future. It also helps when it comes to getting the right funding allocation for your Big Data management project. It is extremely important to link the justification and ROI of Big Data management projects into core business value from the very outset, which many businesses fail to do. This usually results in ineffective management of Big Data. It is best to avoid such eventualities by setting clear expectations, definitions and justification concretely at the very outset.

  1. Understanding how efficient Big Data management helps your business use Big Data more effectively

    This is an extremely important step that no business should miss. Data extraction technologies, the processing of structured data sets and Big Data management are all mutually symbiotic parts of the same process. When it comes to an organization, the most important content that you can ever have access to, is master data that is well structured.Managing this foundational data effectively lays the foundation stone for effective data management, and will equip your business to scale its data processing and management capabilities up or down depending on the amount of data flowing in from the world of Big Data. Big Data management should always keep in mind the broader, eventual goal of being able to handle more data.

  1. Having a sound IT strategy

    It is essential for effective, efficient Big Data management. Since your IT framework forms the backbone of all your data processing and management efforts, you need to ensure that your IT strategy addresses two key points – 1) Ensure that your data management hardware supports real-time governance and control, and 2) Ensure that your IT platform is itself sustainable and scalable for future use.

  2. Choosing the best tech platform available

    A focus on continuing to do this, as technology develops further, is one of the best ways you can stay on top of Big Data management requirements. Advancements in the world of data storage, manipulation, processing and management mean that you can choose between a lot of different software, architectures and platforms for every single one of your requirements. Whether you are using standardized solutions or modified, custom-made best-in-class alternatives, always pay attention to the pros and cons involved. What you need is an efficient, cost-effective and versatile technology platform that can accommodate all your Big Data management needs at present and in future.

  3. Pruning and decluttering your Big Data as soon as you can into the process

    This step has a number of important advantages. Many companies make the mistake of storing all the data they acquire in their original raw form, in spite of the fact that much of it is likely to never be used. It is a way of playing safe and trying to house as much data as possible in the hope that sometime in future, there might be need of it.However, there is every chance that parts of this data pool might be unfit for the particular requirements of a business. For example, overhead data generated from internet interactions and network jitter data are also parts of Big Data, but they have no importance in a number of requirement contexts. Figuring out parts of the Big Data you work with that you can prune without affecting your expectations and requirements can be a good way to end up with a smaller, yet more relevant and effective data pool which is both easier and more cost-effective to store and manage.

  1. Establishing governance standards

    This is the last, but definitely not the least in importance, in this list of best practices for Big Data management. Depending on business use cases of Big Data, there should always be a solid, dependable governance system in place which should dictate all procedures concerning your use of Big Data and its management. Governance standards should dictate explicitly who the people with access are, and how much access is granted to them on an individual level. It should determine if any of the data would be acceptable in a public cloud environment. Standards should also ideally be in place for identifying and better managing data privacy and security issues effectively.

With these seven best practices for Big Data management in the IT sector, you can have a well-oiled, functional, high performance Big Data management system that can do wonders for your business. Starting from basic data collection to complex forecast calculations and the formation of business strategy in a data-driven way, these practices can always ensure that you get to make the most of Big Data.

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