In many years, business intelligence (BI) had been and still is the only heterogeneous process in companies. This is because there is constant communication between enterprise data warehouse (EDW), operational data store (ODS) and real time data processing. It is still a very popular process and there are very small chances of eliminating the use this process in the near future. However, as the world’s digital data grows, there exists a limitation on which technological tools and innovations that can assist in handling big data.
What is BI?
BI is a technology-driven process for analysing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions in a timely manner. Past and recent research results have been constantly showing that the world’s digital data is doubling every 2 years and current technology cannot cope with the volume of such data increase in the future.
Furthermore, traditional data management and business intelligence have become impractical and more organizations do not get what they demand in terms of insight to attain a better business performance. The size of available data is growing faster than our ability to deal with it with the current BI technologies, and more is coming.
Big data is not replacing current BI but also trying to modernize it by adding relevant technologies.
Business managers are struggling to take right decision at the right time. They want to see the information in near real time and execute accordingly. In reality, corporate executives are spending too much both in time and other resources to find a right solution for everyday business problems and save cost for healthy business investment in real-time decision-making.
How traditional BI cope the fast growing data?
It is not easy for storing and analysing large and varied pools of structured transaction data and unstructured information coming from any sources such as sensors, web logs, and social networks microsites. Technologies such as ETLs are too slow or too complex to aggregate such data in real time.
Many companies are facing challenges in analysing data in real time and finding a solution that can handle the velocity at which big data is being created on a daily basis. No matter how many resources you have, real-time business intelligence for big data is definitely more complicated than many people think. Everyone is facing a complex architecture and many tools that are troublesome to integrate to each other for analysing and solving real world business problems. Furthermore, growing data is unstoppable and every organization must be able to discover, store, process, protect and analyse all data that matters, no matter what it is, where it is or how fast it’s growing daily.
Structured, Unstructured and semi- Structured data.
We live in a world where unstructured data is the fastest growing data world ever seen. From an expert’s point of view, unmodernised business intelligence (BI) will not cope with the fast growing unstructured, semi-structured and structured data with the existing slow, static and complex BI tools.
What is unstructured data?
Unstructured data is digital ads, web logs, social media and mobile data. While structured data is marketing, automation, customer relationship management (CRM), transaction, demographic data, enterprise data warehouse (EDW), database (DB), and communication data models (CDM) for telecommunication industry and so on.
Challenges of Business Intelligence Data Processing
The biggest challenges arise when applying real-time BI and data analytics processes to be big data in the initial round of big data processing and management. Today Hadoop and Spark are the best in the market for storing, distributing and analysing information with a reasonable process in batches or real time. However, as the data grows faster than technology, one has to think how best they can cope in the future and solve the challenges ahead.
Hadoop is an open source framework born from java code developed by internet giants such as Google and Yahoo to manage their massive digital volumes of data in a distributed approach on large clusters of commodity hardware. However, Hadoop’s primary technologies; MapReduce and Hadoop distributed file system, are both oriented to batch processing and not real time. Apache Spark framework is the only popular technology for data analysis in real time that is 100X faster than Hadoop’s MapReduce and chances are that it will replace Hadoop MapReduce algorithm in the future.
Real time data ingesting.
The most popular open source technologies are Apache flume, flink, sqoop, storm but they cannot match HPE IDOL connectors of 400+ that enable us to extract any data, any data source and any data size. HPE’s technology for data ingesting is one of the unmatched technology that either open sources or any vendor has ever developed.
Raw data processing to machine-readable form is the biggest challenge that the world is facing today, which is the easy flow of data through the computer’s Central Processing Unit (CPU), on memory to output devices, and formatting or transformation of output. Hadoop is the best technology that can process data in batches and distribute it to multiple clusters. However, it is crucial to note that ETL are required in some areas where data transformation is necessary.
There are different analysis technologies out there that can help in the analysis of both structured and unstructured data, process, inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Consequently, HPE IDOL has a built in data analysis algorithm that has all functions you need to analyse data in real time and create segments in the agnostic language.
HPE Haven technologies will enable your organization:
- Open bits of knowledge on advantages using all your information and make genuine business through proper analysis.
- Settle on information driven sound business decisions continuously and accomplish more great business agility.
- Recognize your existing information resources and framework as worth differentiators; Minimize risks, costs, as well as quality technology obsolescence
- Experience delivery capability with gifted experts on a worldwide scale.
- Wipe out the unpredictability of extract, transform and load (ETL)
- Enhanced knowledge, enhance data streams and decrease information support cost by 70%
- Enhanced productivity and general quality, decreased cost EDW advancement worth to client’s improved hierarchical dexterity and expand income from better services