Business İntelligence and Data Warehousing

business-intelligence-data-warehouse

BI and Data Warehousing: Do You Need a Data Warehouse Anymore?

For quite a while, Business Intelligence and Data Warehousing were practically interchangeable. You were unable to do one without the other: for convenient examination of monstrous historical data, you needed to organize, aggregate and summarize it in a particular format within a data warehouse.

Yet, this reliance of BI on data warehouse infrastructure had a tremendous drawback. Historically, data warehouses were or can be a costly, scarce resource. They take months and a huge number of dollars to setup, and in any event, when set up, they permit without a doubt, very explicit sorts of examination. On the off chance that you have to pose new inquiries or process new sorts of data, you are confronted with major development efforts.

We’ll characterize business intelligence and data warehousing in a modern setting, and raise the subject of the importance of data warehouses in BI.

We offer two alternatives to a traditional BI/data warehouse paradigm:

  1. Instant BI in a data lake using an Extract-Load-Transform (ELT) strategy
  2. Automated data warehouses that allow faster time to analysis without formal ETL

What is Business Intelligence and Analytics?

Business intelligence (BI) is a process for investigating data and deriving experiences to assist businesses with deciding. In a powerful BI process, analysts and data researchers discover important theories and can answer them utilizing accessible data.

For instance, if management is asking “how would we improve conversion rate on the website?” BI can distinguish a potential reason for low conversion. The reason may be absence of commitment with website content. Within the BI framework, analysts can demonstrate if commitment really is hurting conversion, and which substance is the root source.

The tools and advances that make BI conceivable take data—stored in records, databases, data warehouses, or even on gigantic data lakes—and run queries against that data, regularly in SQL format. Utilizing the query results, they create reports, dashboards and visualizations to help extract bits of knowledge from that data. Experiences are utilized by heads, mid-management, and furthermore workers in everyday operations for data-driven choices.

What is a Data Warehouse?

A data warehouse is a relational database that aggregates structured data from across an entire organization. It pulls together data from multiple sources—much of it is typically online transaction processing (OLTP) data. The data warehouse selects, organizes and aggregates data for efficient comparison and analysis.

datawarehouse

A data warehouse keeps up strict accuracy and integrity utilizing a process called Extract, Transform, Load (ETL), which loads data in clumps, porting it into the data warehouse’s desired structure.

Data warehouses provide a long-range perspective on data over time, zeroing in on data aggregation over transaction volume. The parts of a data warehouse incorporate online scientific processing (OLAP) motors to empower multi-dimensional queries against historical data.

Data warehouses applications integrate with BI tools like Tableau, Sisense, Chartio or Looker. They empower analysts utilizing BI tools to explore the data in the data warehouse, plan speculations, and answer them. Analysts can likewise leverage BI tools, and the data in the data warehouse, to create dashboards and periodic reports and monitor key metrics.

Business Intelligence and Data Warehousing: Can You Have One Without the Other?

Twenty years prior most organizations utilized choice support applications to settle on data-driven choices. These applications queried and reported directly on data in transactional databases—without a data warehouse as an intermediary. This is similar to the current trend of storing masses of unstructured data in a data lake and querying it directly.

Colin White records five difficulties experienced back in the times of choice support applications, without a data warehouse:

  1. Data was not usually in a suitable form for reporting
  2. Data often had quality issues
  3. Decision support processing put a strain on transactional databases and reduced performance
  4. Data was dispersed across many different systems
  5. There was a lack of historical information, because transactional OLTP databases were not built for this purpose

These, among others, were the reasons practically all enterprises embraced the data warehouse model. Each of the five of these problems actually appear to be relevant today. So would we be able to manage without a data warehouse, while as yet empowering effective BI and reporting?

BI and ETL: Running in a Data Lake without a Rigid ETL Process

With the advent of data lakes and technologies like Hadoop, many organizations are moving from a strict ETL process, in which data is prepared and loaded to a data warehouse, to a looser and more flexible process called Extract, Load, Transform (ELT).

datawarehouse

Today ELT is predominantly utilized in data lakes, which store masses of unstructured information, and innovations like Hadoop. Data is unloaded to the data lake without much preparation or structure. At that point, analysts recognize relevant data, extract it from the data lake, transform it to suit their examination, and explore them utilizing BI tools.

Does the Data Lake Replace the Data Warehouse?

ELT is a workflow that empowers BI examination while evading the data warehouse. Yet, those equivalent organizations that utilization Hadoop or similar tools in an ELT paradigm, actually have a data warehouse. They use it for critical business examination on their central business metrics—fund, CRM, ERP, etc.

Data warehouses are as yet required for similar five reasons recorded previously. Raw data must be prepared and transformed to empower investigation on the most critical, structured business data. On the off chance that management needs to see a week by week revenue dashboard, or an inside and out investigation on revenue across all business units, data should be organized and approved; it can’t be sorted out from a data lake.

Will such a structured investigation occur without a rigid ETL process? Or in other words, are ELT strategies relevant inside the data warehouse?

BI in an Enterprise Data Warehouse without ETL

New, robotized data warehouses, for example, Panoply are changing the game, by permitting Extract-Load-Transform (ELT) within an enterprise data warehouse.

Array makes it conceivable to stack masses of structured and unstructured data to its cloud-based data warehouse, without any ETL process whatsoever. It utilizes a self-streamlining architecture with machine learning and natural language processing (NLP) to consequently prepare data for investigation. Analysts can run queries to transform the data on the fly varying, and work on the transformed tables in a BI instrument of their decision.

Array takes care of each of the five problems presented above without the expense and complexity of an ETL process:

  1. Data not in suitable form for reporting — Panoply prepares and optimizes data automatically as it is ingested to the data warehouse.
  2. Data has quality issues — Panoply uses machine learning and NLP strategies to automatically correct many quality issues. You can fix other issues using on-the-fly transformations. Or, you can integrate with lightweight ETL tools like Stitch or Blendo, and build a cloud-based ETL pipeline in just a few clicks.
  3. Strain on transactional database performance — not a problem because data is still being loaded to a separate data warehouse.
  4. Data dispersed across many systems — Panoply integrates with dozens of data sources, so loading data is only a matter of selecting a data source, providing credentials and selecting a destination table.
  5. Lack of historic information — Panoply makes it possible to ingest multiple layers of historic information into the data warehouse, and easily join or aggregate the data using on-the-fly queries and transformations.

The primary benefit is shorter time to analysis. With an automated data warehouse, you can go from raw data to analysis in minutes or hours, instead of weeks to months.

From Monolithic Data Warehouse to Agile Data Infrastructure

Data warehouses have made some amazing progress. The monolithic Enterprise Data Warehouse (EDW), which required a multi-million dollar project to setup, and permitted truth be told, very limited BI investigation on explicit kinds of structured data, is destined to be a relic of days gone by.

Today there are two speedy, minimal effort approaches to get from raw data to business experiences:

  • Data lake with an ELT strategy — does not allow the same critical business analysis as the EDW. But a data lake lets you do more with BI, extracting insights from enterprise data that was not previously accessible.
  • Automated data warehouse — new tools like Panoply let you pull data into a cloud data warehouse, prepare and optimize the data automatically, and conduct transformations on the fly to organize the data for analysis. With a smart data warehouse and an integrated BI tool, you can literally go from raw data to insights in minutes.

The slow-moving ETL dinosaur is not acceptable in today’s business environment. Organizations are saving money and making business decisions faster, by simplifying and streamlining process the data preparation process.