Skip to main content
Page Tittle
How to get Business Intelligence (BI) right with Data Lake?
Images
How to get Business Intelligence (BI) right with Data Lake?

What is Business Intelligence - Is it just about dumping all the data from each functional business unit into a huge cloud storage bin? Hooking multiple tools to the current structure to read between lines? Running a bunch of ‘proven’ algorithms to obtain colorful dashboards filled with graphs and charts? We believe Business Intelligence goes way beyond Information Management. Sure, BI involves all the above, but the primary aspect is to add context to the data with an objective to churn easy-to-digest Insights out of it, which can then aid strategic and tactical business decision making.

Traditionally, your Business Intelligence process is only as good as the quality of your Data Repository. That is because the characteristics of baseline data determine the efficiency of the derived Insights.

OLAP Vs OLTP

Modern Data Repositories popularly follow one or both of these two data processing models - Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP).

OLTP manages high volumes of data, including day-to-day transactions in real-time for specific business operations. OLTP enables ease of use and self-service for the end users. Data Lakes and other DBMS systems follow the Transactional data processing.

OLAP handles relatively low volumes of data, requires integration of various data analyzation tools, and offers problem-solving business insights. OLAP improves the productivity of business analysts. All the Data Warehousing systems follow the Analytical data processing.

In simple terms, it is moderately less complex to achieve OLAP when you have your OLTP in order. In contrary to the popular OLAP vs OLTP debate, a hybrid data management system creates an ideal ecosystem that brings the best of both worlds can enable reliable Business Intelligence.

Before we go any further, let me clear something. OLTP systems or Data Lakes are an excellent investment for the business that own high-performing applications that collect, consume, and serve larger number of transactions. 4 signs that your business is ready for a Data Lake Investment.

  1. When the operational complexity of your business exceeded the data capability of resources that have been managing them and there is a demand for a more flexible shared data resource
  2. When your business finds the need to scale your IT operations while lowering the cost of data collection and ownership
  3. When your business objectives can benefit from real-time analytics and the existing data analysis applications are not serving the purpose
  4. When you have in-house data science talents and you are looking for multi-protocol analysis to scale your data management program

In a broader sense, Data Lake is a data storage repository that can collect huge amounts of data in their own raw formats without disturbing its authenticity. But with right data expertise in action, a business can benefit from Data Lake by taking full advantage of its various benefits.

data-lake-infographic

How do Data Lakes enhance Business Intelligence?

Business Intelligence is the computing capability of an organization that is enabled through a governed-yet-flexible analytics ecosystem. That ecosystem is generally built on top of a resilient shared data repository to collect, store, and analyze enterprise data that is individualized yet holistic to the organization. Unlike Data Warehouses that follow a schema-based design, Data Lakes follow data-driven design. Here’s how you can build a robust Data Lake that benefits your Business Intelligence strategy.

  • Have a single shared repository of collected data. A data lake must keep data in its raw format while capturing all the modifications and contextual semantics in real-time throughout the data life cycle. This practice significantly improves the data quality and maintains proper compliance.
  • Ensure orchestration and scheduling capabilities: If your Big Data repository doesn’t come with workload execution, it cannot be called a Data Lake. Look for in-built resource management feature and a central platform to perform regular operations, handle security, and data governance. The analytical workflows require access to the data for uninterrupted Insight generation.
  • A library of pre-set workflows to execute: One of the key requisites of Data Lake is, every consumer to have essential access to the data. Democratization of Data is crucial in Data Lakes. Customer, Supplier, Market, and Operations data is stored in its native form with minimal to no roadblocks to access.

Here are some of the key features of Data Lake that accelerate the process of deriving Business Intelligence in an organization.

Data Tolerance: Data Lakes can absorb real-time transactional data in the format that it comes in without restricting it to any schema. Data Lake can ingest Structured, Semi-structured, and Unstructured data. This gives businesses a bigger scope of exploration and visualization.

Agility: Data Lakes are highly agile. Since these architectures are designed to collect all the data and store them in their original format, Data Scientists can run Data Models rapidly for given business use cases and offer reliable and actionable insights almost instantly.

Low Maintenance: It might take some obvious investment and a few skilled resources to set up the Data Lake, but it is a very cost-efficient way to store mass amounts of data, including the current transactions along with historical information.

Flexibility: Enterprise Data Science practices of recent times have proven that Hybrid data management models that include both Data Lakes and Data Warehouses are high performing. We can now effortlessly integrate an OLAP system in parallel to the Data Lake to replicate the data and gain analytical advantage from the real-time data. It just needs a corresponding ELT pipeline to derive analytics and Business Intelligence.

As much as we love having a hold on every blob of data, if not organized well, there is a very good chance for a Data Lake to become a Data Swamp. So, while you are signing up for a Data Lake expecting a convenient, scale-at-ease big data repository, ensure you have a strategy in place for identifying, aggregating, segregating, and analyzing data.

Does Big data give you cold feet? Qentelli has a proven record of successfully transforming businesses and bringing them closer to their digital, operational, quality, and data objectives. Our experts are just a mail away. info@qentelli.com