Throughout the rest of the decade, spending on big data technologies helps companies to continue at a breakneck pace. IDC’s Worldwide Semiannual Big Data and Analytics Spending Guide shows the true statement. It portrays that the enterprises are likely to spend around $150.8 billion on big data and business analytics in the year 2017. It also tops around $210 billion by the year 2020.
Dan Vesset, Group Vice President at IDC, said, “After years of traversing the adoption S-curve, big data and business analytics solutions have finally hit the mainstream.
This category of solutions is also one of the key pillars of enabling digital transformation efforts across industries and business processes globally.” The company also believes that by the year 2020, enterprises will spend $70 billion on big data software.
Industrial investments of Big Data technologies
Are you aware of how much the industries are spending on implementing big data technologies?
Through several IT leaders and executive surveys, the real phase and the emergence of big data technologies have lent credence. The New Vantage Partners Big Data Executive Survey 2017, found that around 95% of Fortune 1000 executives have invested in big data technology in the past 5 years.
Few cases include huge investments with 37.2% of respondents. They quote that their companies have been spending a huge amount of about $100 million on big data projects particularly.
Taking a geographic perspective in mind, the United States stands ahead in spending the most which scroll around 52%. Western Europe stands second in this list with the biggest regional market reaching nearly a quarter of spending. Added to it, the fastest growth is happening in Latin America and the Asia/Pacific region.
How this big data trend impacts small business owners?
Though many countries have been experiencing emerging big data technology in different sectors, there are still some areas lacking in SMB’s (Small and Medium Businesses).
Here are the major big data technologies to focus upon. Having a glance at it brings in an overview of the further steps to implement big data in your organization.
- The Hadoop Ecosystem
- Data Lakes
- NoSQL Databases
- Predictive Analytics
- In-Memory Databases
- Big Data Security Solutions
- Big Data Governance Solutions
- Self Service Capabilities
- Artificial intelligence
- Streaming Analytics
- Edge Computing
- Prescriptive Analytics
The Hadoop Ecosystem
Days, when Apache Hadoop is dominant in the big data, has faded. Now, it has become impossible to mention big data without including the open-source framework to process large data sets. According to Forrester prediction, 100% of the large enterprises have been adopting big data and its related technologies within the next two years.
Over the years, Hadoop has become the best big data concept that encompasses an entire ecosystem. Taking Zion Market Research into account, we can understand that Hadoop-based products and services are continuing to grow with a percent of 50 CAGR through the year 2022. It’s worth comes around $87.14 billion that ranges from $7.69 billion in 2016.
Few popular Hadoop vendors such as Cloudera, Hortonworks, and MapR are offering services that support big data technology.
Spark places as a part of the Hadoop ecosystem that is prevalently used to process big data. It stands as an engine to process big data within the Hadoop concept. Compared to the standard Hadoop engine, Spark is 100 times faster.
Around 25% of the respondents of AtScale 2016 Big Data Maturity Survey have already deployed spark in their production work. In that part, around 33% were applied in the development phase. Interest over Big Data and Hadoop technology has been sizable and are growing spontaneously. Hadoop offering vendors are also offering Spark-based products.
R, coming under another open-source framework of big data, is an effective programming language. It has been designed to work along with the statistics. R, sorted to be the darling of the data scientists, has been managed under R foundation. Many popular development environments have been supporting the language to a great extent.
Many organizations having programming languages in mind confines that R has become the most popular language widely used. IEEE also confirms it saying that R is the fifth most popular programming language with Tiobe and RedMonk at 14th place.
The significance is mainly because the language has been used for general-purpose that is indulged in different types of work. As the R language is widely used, the prevalence of big data and its importance in sectors have also improved.
Data Lakes, as the name represents, are huge data repositories. They used to collect data from different sources and store it in its natural state. It makes easier access to vast data stores and helps enterprises greatly. This is the reason why enterprises have been indulged in using data lakes widely.
It differs greatly from a data warehouse. As the data warehouse collects data from disparate sources, it processes and structures it in storage. It is the case where data resembles water, it is natural and is stored by collecting it from different sources.
Here, the lake and warehouse metaphors are fairly accurate. A data lake is a natural form and is unfiltered as it comes from different sources. But, a data warehouse is something like a collection of water stored in bottles.
Data lakes are attractive to the enterprises as it enables them to store data. But, they are not sure about how to use it. Still, in the evolving phase, they are moving forward to make it alike. A lot of IoT data has made fit to the category thereby playing the growth of the data lakes.
MarketsandMarkets prediction says that data lake revenue will grow from $2.53 billion in the year 2016 to $8.81 billion in the year 2021.
Data stored in the structured, defined columns and rows have been manipulated and managed through knowledgeable developers and database administrators query. In order to manage this data, they ought to use a specific language, SQL.
NoSQL databases are the sort of unstructured data that provides fast performance. Compared to RDBMSes, NoSQL databases do not provide the consistency level. Popular NoSQL databases including Redis, Couchbase, MongoDB, etc offer NoSQL databases.
When big data trend gMongoDBives a hype, NoSQL databases are also increasingly becoming popular. According to Allied Market Research, by 2020, the NoSQL market will reach $4.2 billion.
Based on historical data, the predictive analysis attempts to forecast future events or behavior. With the subset of big data analytics, prediction becomes easier. It draws in prediction over data mining, modeling, and machine learning techniques. Added to it, predictive analysis has also been used in fraud detection, credit scoring, marketing, finance, and other business analysis purposes.
Advances in AI extended bonus improvements in various predictive analytics codes. With the help of predictive analysis, enterprises have started investing in big data solutions. Vendors including Microsoft, IBM, SAP, and others are providing predictive analytics solutions.
According to Zion Market Research, the predictive analytics market has generated a revenue of around $3.46 billion in the year 2016. And, it has been estimated to reach around $10.95 billion by the year 2022.
Using the memory storage RAM in any computer system, the long-term storage has been boosted up. Rather than data stored on a hard drive, a big data analytics solution will help to access the data in the memory. using in-memory database technology, the process can be boosted up dramatically faster.
Microsoft, Oracle, SAP, and IBM have been offering in-memory database technology. Added to it, many market types of research have been insisting on the importance of an in-memory database for an enterprise. According to MarketsandMarkets, the total sales of in-memory technology had come around $2.72 in 2016 which will hype to $6.58 billion in the year 2021.
Big Data Security Solutions
Big data repositories have been posing an attractive target to hackers. In order to refine the process, big data security has emerged. It is a large and growing concern for enterprises. In the AtScale survey, security is the second-fastest-growing area related to big data.
Dozens of vendors have been moving towards big data security solutions. For example, Apache Ranger, an open-source project has also been attracting growing attention. IDG report says that identity, access controls, data encryption, and data segregation are included under the popular types of big data security solutions.
Big Data Governance Solutions
Security and governance are the terms that overlap each other. Data governance includes a broad section of availability, data integrity, and usability. It helps in getting the assurance of the data used in big data analytics. Using this scene, the audit trail will become easy. Business analysts and executives are able to see the data organization.
According to the NewVantage Partners Survey, around 91.8% of Fortune executives feel that governance is either critically important or important toward big data initiatives. Vendors including Informatica, SAP, and Collibra are providing big data governance tools.
Many enterprises are looking for big data analytics tools to satisfy their own needs on a self-service basis. As data scientists and other big data experts are commanding over large salaries, it is necessary to get the impact of self-service capabilities.
A report from Research and Markets, the self-service business market has generated around $3.61 billion in the year 2016. It might grow around $7.31 billion by the year 2021.
Taking advantage of the trend, several multiple business intelligence and business analytics vendors including Microsoft, IBM, Splunk, TIBCO, Oracle, and others are providing self-service services.
The concept of AI has been revolving around in the digital sector for years. Hence, technology has become truly usable over a couple of days. The big data trend has advanced in AI and there are nearly two subsets namely deep learning and machine learning.
According to experts, the big data tools have been poised with a dramatic takeoff. IDC made a prediction that spotlights around 2018, 75% of the enterprises will include ML as their functionality in their business analytics tools.
Vendors including Google, IBM, Amazon Web Services and a plethora of startups have been indulging in AI technology and are acquiring larger technological vendors.
The capabilities of big data are huge. With the help of big data analytics, companies have started demanding faster access to insights. In this case, enterprises are looking out for a solution that accepts inputs from multiple sources and is processing it in return. Whilst considering new IoT deployments, the particular streaming analytics will help in driving the interest.
Vendors including DataTorrent, SQLstream, Cisco, Informatica, etc have been indulged in providing products compromising the streaming analytics capabilities. MarketsandMarkets also believe that the revenue of streaming analytics will reach around $13.70 billion by the year 2021.
Not only spurring in streaming analytics, but IoT has also been using edge computing. It is the opposite of cloud computing. Instead of simply transferring data toward a centralized server, the edge computing system is there to analyze the source of the data right from the edge of the network.
Enterprises are mainly using the edge computing system in order to reduce network traffic and related costs. It also reduces data centers and cloud computing facilities. At one point, it eliminates the single point failure in a network.
Underlying bitcoin digital currency, blockchain is the distributed database technology. Being a unique featured database, blockchain is highly secured and thereby makes an excellent choice over big data applications. It includes sensitive industries including banking, insurance, healthcare, and others.
Blockchain has still been developing and its use cases are still evolving. With vendors like Microsoft, AWS, IBM, etc have rolled out the experimental and the initial solutions built on blockchain technology.
According to analysts, big data analytics tools can be classified into four categories including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. These categories help in deviating the most analytics tool currently based in the market.
Here, prescriptive analytics offers advice to companies regarding their desired results. It may include a warning regarding the market. For example, if the product line goes down, then the prescriptive analysis will help in taking a course of action and thereby responding to it with the market changes. It also helps in forecasting the most likely results.
As of now, very few companies have invested in prescriptive analytics. But, organizations have been planning to invest huge areas in implementing prescriptive analytics. The market has been diversely changing and thus both predictive and prescriptive tools will become the next big thing in the big data.
In a nutshell, buoyed by the super-fast 5G wireless networks, what is happening in the world will reflect on every citizen’s life. Consumers will be able to interact with a multitude of devices and thereby provides a zone of personalization wherever you go. Progress over big data has crossed a multitude of fronts. Be ready to enhance the benefits of big data.