Big data analytics is near the top of every CIO’s agenda. All companies are faced with an explosion in the volume and variety of data that they have to deal with. There is simply too much for traditional analytics techniques and solutions to cope with. Big data analytics delivers the potential to unlock actionable insight in this mountain of structured and unstructured data. But what should you look for from the best big data analytics solutions and what are the benefits?
The amount of data in the world today is mind-boggling. By 2020, according to IDC, there will be enough data to fill a pile of iPads stretching from the earth to the moon 6.6 times. This is increasing by 16.3 zettabytes annually (a zettabyte is one trillion gigabytes to you and me). There is incredible value locked in that data, but the complexity involved in obtaining it is a challenge. That’s why IDC estimates that the market for big data analytics tools will reach $210 billion by 2020.
What is big data analytics?
Technopedia defines big data analytics as “the strategy of analyzing large volumes of data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who created it. Through this insight, businesses may be able to gain an edge over their rivals and make superior business decisions.” You can find a more thorough definition here.
The best big data analytics platforms comprise a series of analytics algorithms, techniques and tools that enable the advanced analysis of vast amounts of different data in real time or near-real time. They analyze data from a wide range of structured and unstructured data sources quickly and without placing more demands on your IT department, returning results that are easy to understand. These big data analytics tools can include data mining tools, data modelling tools and interactive analytics dashboards.
Common analytics techniques that you should look for include decision tree analysis, cluster or clustering analysis (grouping data together to help identify trends and patterns), forecasting analytics (focusing on data to develop numerical forecasts) and segmentation analytics (For example, customer segmentation uses customer data – demographic, behavior patterns, buying trends – to define highly accurate profiles of specific groups of customers).
Core capabilities of a big data analytics platform
Below is a short list of the core capabilities that the best big data analytics tools should deliver:
With data constantly flowing in and out of an organization, it’s important to establish repeatable processes to build and maintain standards for data quality. The big data analytics tool you choose must have capabilities like data harmonization and data cleansing to help ensure the quality and integrity of the data going into the analytics engine and the results coming out.
Data mining capabilities help you examine large amounts of data to discover patterns and trends in the data. You can cut through the “noise” in big data to identify what’s relevant and use only relevant data as the basis for your analysis.
For this to be successful, you’ll require a data lake.
A data lake provides a massive, scalable repository to store large amounts of data. It’s generally based on a large-capacity database.
The power of the data lake is that it can handle both structured and unstructured data. It can identify the data and draw it from a wide variety of internal and external sources. The data is then normalized (turned into a standard format) to enable it to be analysed. This helps overcome the “garbage in, garbage out” challenge of data quality.
Big data modelling
Once you have your big data in a common structure, you can begin to model the data. Big data modelling sets out the attributes of a specific piece of data and the relationships between data and data sets.
Traditionally, analytics has been applied to data that was stored on a hard disk. The data was historical and the analysis reported what had happened because searching all the data on the disk could take hours or days. More advanced forms of big data analytics require data access in real time. The in-memory capabilities of big data analytics software access the data from the system’s memory almost instantly. This way, you can derive immediate insights from your data and act on them quickly.
In-memory analytics is likely to be required when using predictive analytics or prescriptive analytics – especially where you are deploying advanced capabilities such as machine learning or AI-powered analytics.
Predictive analytics technology uses data, analytics algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. An effective big data analytics tool leverages predictive analytics to provide a best assessment on what will happen in the future, based on previous experience, delivering real insight to drive decision-making.
Predictive maintenance and fraud protection are just two of the areas where predictive analytics is already making a real impact. For example, the US Centers for Medicare and Medicaid Services report saving over $1 billion between 2014 and 2015 by using predictive analytics to prevent fraudulent transactions before they happened.
When you think of big data, it’s easy to think of it as a huge mass of numbers in a database or EDI document. In fact, this is becoming less and less true. Today, the majority of new data in an organization is unstructured and the majority of that data is held within content – web pages, emails, Microsoft Word files, presentations, videos, and social media posts. The big data analytics solution you choose should be able to integrate big content as well as big data. Effective content mining uses machine learning or text mining to unlock the insights held in content.
There is no effective way to manage and analyze unstructured data without metadata management – managing the data that provides information, classification and indexing on content. Your big data analytics software should automatically tag and categorize data to allow you to quickly ingest, prepare, manage, analyze and retrieve data.
Metadata management has an important role to play in an effective and robust data governance strategy. Big data analytics poses a challenge to data governance and compliance as it requires the bringing together of data from different sources. Governance elements such as data lineage (where has the data come from and what has happened to it over time?) and data stewardship (the management and oversight of your data assets) require visibility of all data in the organization. Big data analytics solutions with metadata management help by enabling specific pieces of data to be quickly identified and returned for audit and compliance purposes.
Why choose OpenText for big data analytics?
OpenText™ has been a leading provider of business intelligence (BI) and analytics tools for many years. Our range of advanced analytics solutions includes:
OpenText Magellan is a flexible, AI-powered analytics platform that combines open source machine learning with advanced analytics, text mining, enterprise-grade business intelligence, and capabilities to acquire, merge, manage and analyze big data and big content stored in your Enterprise Information Management (EIM) systems.
Big data can appear intimidating but the truth is that the more data an organization the more raw material it has to deliver more valuable insight to drive decision-making and business performance. Big data analytics is the technology to make this happen. In our next blog, we’ll look at the benefits that big data analytics tools can bring to the manufacturing industry.
Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.