The concept of big data analytics has been around the years. Many people view “Big data analytics” as an over-hyped buzzword. But in the real sense, big data analytics is the method that examines large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other valuable business information. Organizations take magnanimous decisions to harness their data for actionable decisions in a competitive business landscape. For cost reduction, faster & better decision making, and promotion of new products & services, business entities are becoming more oriented to develop data analysis infrastructure. This brings about a robust environment for competitive intelligence and safeguards the continuity of the business for a long time considering the importance of incessant market and technology shifts.
Need of Big Data Analytics
Digital content created globally will increase 30 times over the next ten years and will reach to a whopping value of 35 zettabytes. With the impact of hyperconnected world and network systems, traditional analytical processing technologies are not capable of handling a wide range of data volumes in a timely manner. To meet the situation, many new and evolving analytical processing technologies including new data management systems, improved analytical capabilities, and faster hardware have been emerged. Big data analytics is effectuated for multifarious usages including real-time fraud detection & cybersecurity, competitive analysis, call center optimization, web display advertising, social media & sentiment analysis, traffic management, and business intelligence.
Why Is Big Data Analytics Important?
- It brings significant cost advantages.
- It ensures easy, quick, and better decision-making process.
- It helps companies to create new products and services as per customers’ needs.
- It provides actionable business decisions with high-level transparency.
- It automates business processes.
Big Data Analytics in Cybersecurity
Big data analytics provides a real-world insight into the current state of affairs and acts as a harbinger for the future. With the emergence of big data analytics platforms such as Hadoop and Splunk, searching, monitoring, and analyzing of big data in a distributed computing environment has now become feasible. On the other hand, cyber-attacks are in full spree because of the advanced and sophisticated techniques used by the cyber criminals – whose main intention is to infiltrate corporate networks and enterprise systems. Thereof, various types of cyber-attacks in IT landscape are executed through advanced malware, zero day attacks, and advanced persistent threats. Nearly 1 million malware threats are being released every day and 99 percent of computers are vulnerable to cyber-attacks. Moreover, the estimated cost of cybercrime is up to $1 billion. Astonishingly, less than half of organizations are aware of preventing anomalous and malicious traffic from entering networks or detecting such traffic in their networks. As per Gartner forecast, there will be 20.8 billion digitally connected devices by 2020. The proliferation of connected devices will bring abundance of new data streams and create information overload for many enterprises. In addition, machine and Artificial Intelligence (AI) learning will continue to dominate the headlines in 2017. Forrester predicts companies will invest 300 percent more in AI in 2017 than in 2016. Business entities are intended to keep more focus on big data analytics. Over the years, big data within the enterprise has dramatically changed the nature, structure, and functions of the enterprises and has resulted a new position — the Chief Data Officer to drive innovation and establish a data culture. Furthermore, various techniques are used with a view to harness raw data, such as data mining, which provides an insight into the future of cybersecurity. By analyzing various social media platforms, it is noticed that current trends and key interest points to the population for determining the security traits. Similarly, rising popularity of certain technology also demands the effective implementation of cybersecurity applications. In the realm of cybersecurity, big data analytics has ability to monitor and track systems, usually contained within the cloud. With the emergence of Cloud Security Information and Event Management (CSIEM), secured storage and transmission of user’s private information and files without any fear of being victim to a cyber-attack has become feasible.
To sum up, by gathering massive amounts of digital information, big data analytics is used for effective analysis, visualization, and drawing insights that can make it possible to predict and stop cyber-attacks. Big data analytics is used to identify cybersecurity risks, threats, and incidents, thereby helping businesses to be exempted from vulnerability and cyber-attacks. Thus, big data analytics has become an integral part of cybersecurity. As malware is becoming more pervasive and evasive, data analysts intend to keep focus on malware research & analysis, macro trend analysis, and detection performance measurement for providing protection to threat landscape.
Despite various positive connotations of big data analytics, its use is not up to the mark in the sphere of maintaining the perfect level of cybersecurity. Even big companies continue to scuffle due to terabytes and petabytes of data. In addition, IT teams and security analysts have become overburdened due to ever-growing requests for data and high level of vulnerability. Businesses have witnessed varied challenges in the way of building and deploying the analytics applications to detect irregularities in an IT environment.
Some of the challenges that are faced in big data analytics include:
Difficulty in Data integration: Integrating data both structured and unstructured is very difficult. Normally, data coming from many disparate sources is very difficult to integrate.
High Data volume: Handling a large volume of data in a limited time frame has become a significant challenge for data analysts.
Unavailability of Skills: Big data management is strictly dependent on effective and efficient implementation of tools and techniques. But sometimes, due to lack of knowledge and skill data analyst is not able to implement the right tools. Therefore, selecting proper technology and tool for data analysis is very important.
High Solution Cost: To ensure a Return on Investment (ROI) on a big data project is very difficult due to the cost involved in it.
Big data analytics has opened a new world of business possibilities by ensuring a high level of security. It has brought about some sort of insights that turn to business value. It highlights data analysis technologies and new data management tactics that empower organizations to analyze a wide range of data for taking effective decisions. As a data analyst, you must be well versed in using up-to-date technology to analyze 3Vs (volume, variety, and velocity) — the three defining properties or dimensions of big data. Big data analytics not only seeks to implement new technologies but also requires clarity of understanding of senior management to take smarter decisions. The forthcoming years will be the years of democratization of data analysis. Big data will govern the whole business world. So, for effective big data analytics, it is imperative to build big data strategy for extracting business value, making robust planning for all format of big data, and for building roadmap from legacy to new data platforms; to initiate strong collaboration amongst the BDM organization; to ensure trained staff to work on following clear governance model; and to take pragmatic business decisions considering IT budget.