Big Data Analytics Guide
A Major Aspect Of Digital TransformationAdd bookmark
For enterprises undergoing digital transformation, big data analytics certainly plays a significant role. Organizations are able to process more data in real time, and leverage new tools in order to quickly analyze that data.
Enterprise Digitalization created this guide about the past, present, and future of big data in the enterprise. This guide includes quotes from thought leaders, trends, and important strategies.
What Is Big Data Analytics?
Big data refers to larger and more complex volumes of structured and unstructured data that require storage capabilities beyond normal servers. As a result, it surpasses the capabilities of traditional software tools for management and processing. Big data is often defined by the three V’s:
- A larger volume of data
- An increased velocity of data being received
- New varieties of data formats
Analytics is leveraged to discover obscure correlations and patterns in the data that can lead to increased business value. By processing large amount of complex data, enterprises are able to make more informed decisions.
There are major differences between big data and traditional data that surround the processes, solutions, strategies, and goals of a business. Big data can provide real-time results, it is scalable, and it is tied into the benefits of machine learning. Traditional data programming, on the other hand, cannot handle the immense volume of information.
History Of Big Data Analytics
Enterprises have been relying on basic analytics to find insights since the 1950s, where workers had to manually examine numbers on spreadsheets, which could be incredibly time-consuming. By the 1970s, computers were being used to make sense out of traditional data.
Big data as we know it dates back to the 90s, although it really started to take off around 2012 and 2013. According to Google Trends, searches for this term peaked in 2015.
In the past few years, the use of larger data sets has changed the entire business landscape. Over half of all companies have already embraced data analytics in some way. The influx of new technologies, specifically artificial intelligence (AI) and the Internet of Things (IoT), has paved the way for enterprises to access more data.
According to Statista, 2 zettabytes of data were created by global organizations in 2010. That number jumped to 12 zettabytes in 2015. By 2020, 47 zettabytes of data will be created, and that number will catapult to 163 zettabytes by 2025. IDC estimates that the big data market, which was worth $166 billion in 2017, is on pace to reach $260 billion by 2022.
Industry Experts Explain Big Data
“What's changing over time is that we realize that there is a small portion of generated data that actually should be captured, but is not. We're now able to capture more data,” said Ryan Martin, principal analyst with ABI Research. “After data is captured, it's then transmitted for storage or analysis.”
“Utilizing big data to its maximum potential, across the enterprise, isn’t as easy as picking components from each layer of the stack and plugging them all together or integrating them like Lego bricks,” said Paige Bartley, senior analyst for 451 Research. “Increasingly, vendors from various product heritages are expanding their platforms to include functionality that spans multiple aspects of the data storage, management, and consumption stack: often making the strategic decision between products a difficult one due to overlap in capabilities.”
“IT leaders have several choices in developing big data infrastructure, including on-prem, in the cloud, and hybrid,” said Julius Bogdan, director of analytics and data innovation at SCL Health. “Your decision will be made in large part due to the resources you have, expertise with open source software, and appetite for buy vs build. One technology that can help with bridging the on-prem and cloud environments is containerization, which will allow you to optimize your on-prem assets while giving you the ability to leverage the cloud for big data workloads.”
Big Data Trends
Fueling The Rise Of Cloud Managed Services
The rise of big data is encouraging enterprises around the world to adopt cloud managed services and cloud stockpiling services. According to a report by Transparency Market Research (TMR), the global market for cloud managed services is projected to grow at a 9.60% CAGR by 2022. When that happens, the market valuation will be over $86 billion.
Cloud computing was built perfectly for data. When an enterprise leverages cloud computing, it can securely refine data. Businesses in industries with strict compliances must follow security and privacy guidelines that require authentication and encryption of the data. This is where cloud managed services come in. In many ways, cloud managed services was instrumental in the rise of big data in the enterprise.
Incorporating Virtual Reality
To simplify the process of analyzing big data, enterprises are starting to leverage virtual reality (VR). This technology can change the way that employees in certain industries access, share, and analyze data. VR goes beyond the traditional methods of analyzing data by allowing team members to view an augmented world with a rich dataset.
Traditionally, users would manually examine data with their eyes, but VR allows users to actually touch the data with their hands. It is a more interactive way to see and verify data.
A few years ago, Goodyear leveraged VR to help determine why its tires were underperforming in races. The company conducted racing simulations and accessed game-changing data about its tires in a short period of time.
Big data analytics is becoming a common component of any supply chain. Previously, companies would engage in corrective maintenance, which fixes an equipment after it breaks. This is a data-heavy process; it’s also reactive by nature and outdated.
Today, predictive maintenance is becoming the norm. This involves analyzing larger data sets to determine when pieces of equipment need to go through maintenance before something breaks. The process is achieved with sensors embedded in equipment that could automatically trigger a field service professional to repair something, or alert them when a machine is due for maintenance.
Thanks to the development and implementation of predictive maintenance, companies are able to increase operational efficiencies, enhance customer engagement, and realize new revenue streams. It has empowered field service professionals to take a more proactive stance to maintenance and increase customer satisfaction.
Strategies For Big Data Analytics
Properly Organize Your Big Data Infrastructure
IT leaders typically develop big data infrastructure with a combination of on-premises and cloud-based apps in order to derive the most value out of the data. The simplified data technology landscape is comprised of three distinct layers:
- The database or repository layer as the base. This is an example of cloud blob storage, where data is kept.
- The data management infrastructure layer in the middle, which includes tools to get data out of the repositories, and shape it into usable datasets that can ultimately be fed into applications. This is the layer where functions such as data integration, self-service data prep, data quality, data governance, and master data management occur.
- The application layer at the top where data is generally consumed. This includes a large variety of common SaaS tools, and functionality such as self-service visualization and analytics.
Leverage Data Visualization Tools
Solutions involving data visualization are quickly becoming one of the most popular tools for efficiently comprehending complex datasets. In recent years, more enterprises have been jumping onto the data visualization bandwagon.
Gone are the days when simple charts and graphs would serve as a sufficient visual form of data. Today’s tools can present the data in more sophisticated ways, including geographic maps, scatterplots, bubble charts, and heat maps. All of these images are becoming more interactive, which allows users to easily explore the data for advanced analysis.
There are several trends within visualization solutions today. These tools are leveraging machine learning to enhance data sets that are present in visualization, which is often referred to as visual analytics. Some of the latest data visualization tools are able to weave both historical and real-time data into models. The best tools are now mobile-friendly, which allows users to easily build data visualization models on mobile devices, and view them.
Integrate Data Sources With Hadoop
As organizations seek to leverage data in order to discover new insights and improve the user experience, integrating data sources becomes a leading strategy. When an enterprise integrates traditional data sources, it an achieve business value at a faster rate.
One of the leading solutions behind data source integration is Apache Hadoop, which is open source software that uses clusters of hardware in order to quickly solve data problems. As enterprises collect more big data, Hadoop is increasingly seen as a solution that can analyze the data at a fast rate. Many cloud services now offer Hadoop integration, which is free.
Big Data Challenges And Solutions
Enterprise Digitalization surveyed its audience in March 2019 regarding the top big data IT challenges. Over half of enterprises said that they were tormented by inaccurate or inconsistent data. One of the goals of digital transformation is to enable all knowledge workers to drive value with data, but unreliable or trustworthy data complicates this objective.
As a solution to imprecise data, enterprises are leveraging data cleansing tools, which can rectify or remove inaccurate or duplicated data. The ability to scrub and clean data is critical, especially in certain industries with strict data compliances. Other solutions for fixing inaccurate data involve data validation tools with techniques based in machine learning.
Storing data in different silos and older environments with a limited ability to scale all of the data is another major challenge. In the Enterprise Digitalization survey, one in five enterprises identified this issue as the most significant IT big data obstacle. This is a common issue among enterprises, especially those companies that are just starting to strategize for big data. It can be a highly detrimental barrier because different silos can have different abilities for managing permissions.
As a possible solution, some enterprises are turning to unified data platforms. This technology breaks down the data silos so that enterprises can gain advanced business insights.
Difficult Data Conversations
As raw data is collected, it must be converted into analytics-ready datasets. However, this process might not be efficient and it could be time-consuming. In the survey, over a quarter of enterprises found this to be the most significant IT obstacle.
Data visualization tools are among the solutions that can be leveraged as a remedy. These tools can help enterprises quickly and efficiently comprehend complex datasets.
What Will The Future Of Big Data Analytics Look Like?
The future looks bright for big data analytics, as the data technology market is projected to soar in the coming years. Currently, the use of big data can be best described as maturing. Many providers are still focused on the technology aspect, but the reality is that many leaders in the enterprise realize that it is the means of capitalizing on the data that is more significant.
In January 2019, Teradata CTO Stephen Brobst predicted that businesses in the future will diversify, using niche, best-of-breed solutions geared towards specific uses of the data.
The solutions for big data will also evolve in the future. AI will still play a major role towards the growth of big data, as well as other solutions, including blockchain.
As enterprises prepare for the future of data, it is imperative that IT leaders will play a key role in this process. How can IT leaders create a winning strategy for data? Download our latest report, Big Data: The Ultimate IT Guide, to discover how other IT leaders are stepping up.