Big Data Analytics -Data is a very valuable asset in the world today. The economics of data are based on the idea that data value can be extracted through the use of analytics. Though Big data and analytics are still in their initial growth stage, their importance cannot be undervalued. As big data starts to expand and grow,Importance of big data analytics will continue to grow in everyday lives, both personal and business. In addition, the size and volume of data is increasing every single day, making it important to address the manner in which big data is addressed everyday.
According to surveys being conducted many companies are opening up to using big data analytics in their daily functioning. With rising popularity of Big data analytics, it is but obvious that investing in this medium is what is going to secure the future growth of companies and brands.
The key to data value creation is Big Data Analytics and that is why it is important to focus on that aspect of analytics. Many companies use different methods to employ Big Data analytics and there is no magic solution to successfully implementing this. While data is important, even more important is the process through which companies can gain insights with their help. Gaining insights from data is the goal of big data analytics and that is why investing in a system that can deliver those insights is extremely crucial and important. Successful implementation of big data analytics, therefore, requires a combination of skills, people and processes that can work in perfect synchronisation with each other.
Today, companies are developing at a rapid pace and so are advancements in big technologies. This means that brands must be ready to pilot and adapt big data in such a manner that they become an integral aspect of the information management and analytics infrastructure. With amazing potential, big data is today an emerging disruptive force that is poised to become the next big thing in the field of integrated analytics, thereby transforming the manner in which brands and companies perform their duties across stages and economies.
With great potential and opportunities however come great challenges and hurdles. This means that companies must be able to solve all the concerned hurdles so that they can unlock the full potential of big data analytics and its concerned fields. When big data analytics challenges are addressed in a proper manner, the success rate of implementing big data solutions automatically increases. As big data makes its way into companies and brands around the world, addressing these challenges are extremely important.
Some of the major challenges that big data analytics program are facing today include the following:
- Uncertainty of Data Management Landscape: Because big data is continuously expanding, there are new companies and technologies that are being developed everyday. A big challenge for companies is to find out which technology works bests for them without the introduction of new risks and problems.
- The Big Data Talent Gap: While Big Data is a growing field, there are very few experts available in this field. This is because Big data is a complex field and people who understand the complexity and intricate nature of this field are far few and between. Another major challenge in the field is the talent gap that exists in the industry
- Getting data into the big data platform: Data is increasing every single day. This means that companies have to tackle limitless amount of data on a regular basis. The scale and variety of data that is available today can overwhelm any data practitioner and that is why it is important to make data accessibility simple and convenient for brand mangers and owners.
- Need for synchronisation across data sources: As data sets become more diverse, there is a need to incorporate them into an analytical platform. If this is ignored, it can create gaps and lead to wrong insights and messages.
- Getting important insights through the use of Big data analytics: It is important that companies gain proper insights from big data analytics and it is important that the correct department has access to this information. A major challenge in the big data analytics is bridging this gap in an effective fashion.
This article will look at these challenges in a closer manner and understand how companies can tackle these challenges in an effective fashion.Implementation of Hadoop infrastructure. Learn hadoop skills like HBase, Hive, Pig, Mahout.
The challenge of rising uncertainty in data management: In a world of big data, the more data you have the more easier it is to gain insights from them. However, in big data there are a number of disruptive technology in the world today and choosing from them might be a tough task. That is why big data systems need to support both operational and to a great extent analytical processing needs of a company. These approaches are generally lumped into a category that is called NoSQL framework that are different from the conventional relational database management system.
There are number of different of NoSQL approaches available in the company from using methods like hierarchal object representation to graph databases that can maintain interconnected relationships between different objects. As big data is still in its evolution stage, there are many companies that are developing new techniques and methods in the field of big data analytics.
In fact, new models are being developed within each NoSQL categories, that help companies reach goals.These Big analytics tools are suited for different purposes as some of them provide flexibility while other heal companies reach their goals of scalability or wider range of functionality. This means that the wide and expanding range of NoSQL tools have made it difficult for brands owners to choose the right solution that can help them achieve their goals and be integrated into their objectives.
Choosing a wrong tool can be a costly error as this might not help the company reach its goals and also lead to wastage of time and resources. Understanding this is extremely important for companies as only choosing the right tool and core data magnet landscape is the fine line between success and failure.
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The existing gap in terms of experts in the field of big data analytics: An industry is completely depended on the resources that it has access to be it human or material. Some of the new tools for big data analytics range from traditional relational database tools with alternative data layouts designed to increased access speed while decreasing the storage footprint, in-memory analytics, NoSQL data management frameworks, as well as the broad Hadoop ecosystem. With so many systems and framework, there is a growing and immediate need for application developers who have knowledge in all these systems. Despite the fact that these technologies are developing at a rapid pace, there is lack of people who possess the required technical skill. Another thing to keep in mind is that many experts in the field of big data have gained their experience through tool implementation and its use as a programming model as opposed to data management aspects. This means that many data tool experts do not have the required knowledge about the practical aspects of data modeling, data architecture and data integration.
This lack of knowledge will result in less than successful implementations of data and analytical process within a company/brand.
According to analyst firm McKinsey & Company, “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.
All this means that while this sector will have multiple job opening, there will be very few experts who will actually have the knowledge to effectively fill these positions. While data practitioners become more experienced through continuous working in the field, the talent gap will eventually close. At the same time it is important to remember that when developers cannot address fundamental data architecture and data management challenges, the ability to take a company to the next level of growth is severely affected. This means that companies must always invest in the right resources, be it technology or expertise so that they can ensure that their goals and objectives are objectively met in a sustained manner.
The challenge of getting data into the big data platform: Every company is different and have different amounts of data to deal with. While some companies are completely data driven, others might be less so. That is why it is important to understand these distinctions before finally implementing the right data plan. Also, not all companies understand the full implication of big data analytics. Assuming that every company is knowledgeable about the benefits and growth strategy of business data analytics would seriously impact the success of this initiative. That is why it is important that business development analytics are implemented with the knowledge of the company.
As companies have a lot of data, understanding that data is very important because without that basic knowledge it is difficult to integrate it with the business data analytics programme. Communication plays a very integral role here as it helps companies and the concerned team to educate, inform and explain the various aspects of business development analytics.
Before even going towards implementation, companies must a good amount of time in explaining the benefits and features of business analytics to individuals within the organisations including stakeholders, management and IT teams. While companies will be sceptical about implementing business analytical and big data within the organisation, once they understand the immense potential associated with it, they will easily be more open and adaptable to the entire big data analytical process.
The challenge of need for synchronisation across data sources: Once data is integrated into a big platform, data copies migrated from different sources at different rates and schedules can sometimes be out of sync within the entire system. There are different types of synchrony and it is important that data is in sync otherwise this can impact the entire process. With so many conventional data marks and data warehouses, sequences of data extractions, transformations and migrations, there is always a risk of data being unsynchronized.
With exploding data volumes and rising speed in which updates are created ensuring that data is synchronised at all levels is difficult but necessary. This is because of data is not in sync it can result in analyses that are wrong and invalid. If inconsistent data is produced at any stage it can result in a inconsistencies at all stages and have a completely disastrous results. Wrong insights can damage a company to a great degree, sometimes even more than not having the required data insights.
The challenge of getting important insights through the use of Big data analytics: Data is valuable only as long as companies can gain insights from them. By augmenting the existing data storages and providing access to end users,big data analytics needs to be comprehensive and insightful. The data tools must help companies to not just have access to the required information but also eliminate the need for custom coding. As data grows inside, it is important that companies understand this need and process it in an effective manner. As data size may increase depending on time and cycle, ensuring that data is adapted in a proper manner is a critical factor in the success of any company.
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These are just some of the few challenges that companies are facing in the process of implementing big data analytics solutions. While these challenges might seem big, it is important to address them in an effective manner because everyone knows that business analytics can truly change the fortune of a company. From preventing fraud to gaining a competitive edge over competitors to helping retain more customers and anticipating business demands- the possibilities with business analytics are endless. In the last decade, big data has come a very long way and overcoming these challenges is going to be one of the major goals of Big data analytics industry in the coming years.