What is BDaaS?
Big Data as a Service (BDaaS) gives business customers access to large collections of big data – supported by cloud-based software and analytical tools to manage, analyse and extract valuable business intelligence from large datasets.
Big data represents a rich gold mine of business intelligence for any organisation that knows where and how to dig.
By using data analytics to reveal and analyse patterns within these mountains of big data, businesses can identify trends, extrapolate predictions, test hypotheses and forecasts, research opportunities and more. The resulting intelligence and insights support data-led decision-making.
But most organisations aren't in a position to collect, store and manage such large data sets – amassing decades of rich historical data – when data management isn't the organisation's core activity.
A Big Data as a Service (BDaaS) provider uses cloud computing to make aggregated collections of global big data available to businesses – along with the necessary data warehousing services and analytical tools to find patterns, make connections and reveal valuable insights. Some of these big data collections can be far larger and more comprehensive than most other businesses could ever hope to acquire.
And because big data is stored, managed, manipulated and analysed in the cloud, there's always enough computing power to crunch these large volumes of data.
The Software as a Service model
Softwares as a Service (SaaS) solutions are nothing new, allowing businesses to access powerful, feature-rich software and services without the expense, complexity, ongoing maintenance and risk of committing to a locally installed solution. Instead, cloud computing delivers the necessary scale, flexibility and functionality at an affordable cost.
Big Data as a Service uses this same SaaS model to make available vast collections of big data far beyond what most businesses could collect or manage themselves.
Big data cloud services make it possible for companies of any size to benefit from the mountains of global data produced every day. Big Data as a Service not only stores and manages these incredibly large datasets but also provides the data analytics software to extract meaningful and valuable insights from such a massive amount of information.
The increasing adoption of Big Data as a Service means the global BDaaS market continues to grow rapidly.
The benefits of BDaaS?
While it is possible for larger organisations to handle big data projects in house, the complexity and expense involved in capturing, storing and analysing all the data relevant to the business means it isn't always practical.
Whether you decide to use on-premises data centres or set up a public or private cloud infrastructure, storing large data volumes is likely to be a major cost to your business. Managing your own data storage and data management comes with a bunch of other challenges as well, such as compliance and data protection.
Understandably, the expense and expertise required to capture, organise, store and analyse data on such a scale leave most small and medium-sized businesses behind.
Opting for Big Data as a Service instead means you only pay as you go, without the considerable overheads of running your own data centre. And because the big data services provider handles the ongoing infrastructure management, software updates, compliance and data protection, your business can focus on extracting the maximum value from big data analytics without the headaches and expense.
Of course, there are also the many benefits of conducting data analysis on large volumes of big data.
- Identify business opportunities and increase revenue
- Reduce costs and improve efficiency
- Guide and optimise marketing strategies
- Gather intelligence on customer trends and behaviours
- Respond and adapt quickly to market events and industry trends
- Identify and reduce risk
- Gain a competitive edge
How BDaaS can be used
Big data as a service represents the greatest opportunity for businesses to develop a competitive advantage. Companies can gain insights to inform decision making, guide strategies and drive business growth.
For example, many organisations and large enterprises routinely use big data analytics in combination with their internal analytics to forecast future performance and identify opportunities.
Data analytics is a subset of data science concerned with finding answers to specific questions. Depending on your goals, you might use data analytics in different ways.
Analyses historical data to answer questions about what has happened. Routinely used to track outcomes against KPIs or calculate a project's return on investment (ROI).
Helps to understand why things happened by revealing trends and connections within related data or by identifying anomalies.
Uses historical data to identify trends and forecast potential future outcomes should those trends continue.
Builds upon predictive analytics to assess different courses of action and guide decisions by analysing the outcomes of previous decisions and events.
Big data explained
Big data is an umbrella term encompassing vast amounts of data generated every day by businesses, individuals and devices, estimated to be 79 zettabytes in 2021. Some of that data is made up of small fragments of information, such as a social media update of a few kilobytes. Other larger forms of data include rich media content such as videos, podcasts, animations, etc.
Therefore, there are many different types of data, requiring different analytical tools and techniques to identify patterns and draw insights.
Text heavy and without obvious organisation beyond how the files are stored. Examples include books, videos, photos, phone calls and social media posts.
Highly organised data captured that can be stored in a structured database or table. This makes it easy to search, sort, log, capture and analyse. Examples include sales data, customer information, (names, dates, addresses, etc.), product IDs, and more.
Data that doesn't fit within a structured table but contains semantic tags or meta information. Can be searched or analysed with alternative analytics methods. Examples include emails, web pages, some image files, etc.
The vast majority of big data created every day is unstructured, making effective analysis more difficult.
Content aggregators and BDaaS providers like Nexis® Data as a Service (DaaS) apply granular metadata to archived content – such as news stories and press releases – so that it can be searched, manipulated and analysed effectively.
Nexis Big Data as a Service
Nexis® Data as a Service (DaaS) connects businesses to more than four decades of aggregated content and business data. Extract valuable business intelligence from our vast archive with a series of flexible APIs – all managed from within a single dashboard.
The Nexis DaaS archive includes 45+ years of news datasets:
- Licensed news content: 80,000+ print & web news items from 100+ countries in 75 languages.
- Online news: 4.5 million items added daily, including blogs and social commentary.
- Newswires and press releases: 240K documents added per day, including magazines and trade journals.
- Broadcast data: Covers 1500 channels, including TV and radio transcripts.
Quickly draw insights from a vast collection of semi-structured and enriched news data –automatically aggregated across a range of relevant sources and topics.
The archive also contains a vast collection of legal datasets:
- Company and industry data: Financial reports and corporate hierarchies.
- Legal entity data: Lawsuits, intellectual property (IP), patents and other legal filings.
- Regulatory data: PEPs, sanctions and watchlists.
Nexis DaaS business data includes company profiles and corporate hierarchies, as well as company and financial reports, mergers-and-acquisitions activity and more. Analyse this data collection to reveal and monitor financial trends, track your brand portfolio and gather valuable business intelligence.
Whatever your data-analysis goals, tools and techniques, the vast scope of our data and flexible APIs mean that Nexis DaaS can easily be integrated into your organisation's big data projects and processes.