Data warehouse for modern managers

warehouse In the current generation, companies and customers generate a huge amount of data. Above all, widespread usage of digital devices is pushing this. This is definitely something to cherish. But the question arises how can we practically manage this data in order to get valuable insights. By 2020, the digital universe will expand to 40,000 exabytes (1 exabyte = 1,000,000 terabytes).


Data warehouse (DWH) is the collection and rearrangement of the diverse underlying area, which is spread across various locations. Essentially, we can assess any observable trend in different datasets. It is important for the modern manager to understand the dynamics of data warehouse in the world of data science, data mining and business intelligence.

The data warehouse is a centralized repository of digital information collected from different sources and structured in a way that is suitable for reporting. Matter of fact, it provides workable insight to the enterprise, enabling employees to execute thorough analysis and make better decisions.

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Key concepts of data warehouse

Relational and dimensional database

You must understand the difference between relational and dimensional database to comprehend the essence of DWH. Technically, they are easy to compare. The only difference lies in the information flow. While the relational model focuses more on the input quality, the dimensional model optimizes the output. The dimensional model is the form of reports and analytics, called business intelligence.

The relational model arranges information around a single point of information. Let us consider an example. In a grocery store, we take the case of vegetables. Then it will fill up the different vegetable names. Also, the related data like price, date of purchase, and buyer details, in their respective or related blocks.

Whereas the dimensional database is able to fragment information in the relational database. This way one can easily pick and choose data according to their reporting needs. For those dealing with reporting and data analytics, appreciating the distinction between these two models provides essential elementary information about working with technical teams who manage these resources.

It’s On

Bill Inmon, one of the founders of the data warehouse, has defined it as a subject-oriented, integrated, nonvolatile and time variant collection of information. While it sounds great, for better understanding, let us understand the meaning in accordance with the anagram “It’s On”.

Integrated: The DWH processes consistent information. In this process, it is important to have consistent naming conventions.

Time-variant: The DWH shows trends. As it collects data over time, it is necessary to plot relationships within the data.

Subject-oriented: Data warehouse is a collection of subject-oriented data and reports. For example, if we take a disease report, we can further look into the number of people affected, vulnerable cities and growth trends.

Nonvolatile: Once we enter the data, it does not change.

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Data warehouse vs Transactional database

The data warehouse differs from the transactional database in various factors. The transactional database is also known as OnLine Transactional Processing (OLTP). Let us have a brief look.

Content: The DWH provides historical data, whereas the transactional database system provides current data.

Volatility: The DWH contains nonvolatile data as discussed earlier. But in the transactional database, data is detailed and changes with several transactions.

Purpose: We prefer data warehouse for analytics and reporting. For availability and processing speed, you should choose transactional database system.

Users: Analysts and managers use DWH. Most front-end employees use transactional system.

Editing: You can only read in DWH, whereas you can both read and write data in transactional system.

User interaction: It is according to the requirement in DWH. Pre-defined in transactional system.

Access: You can access millions of records in DWH, but a few records in OLTP.

Focus: DWH focuses on data retrieval. In contrast, OLTP focuses on data writing.

Data warehouse, data mart and data lake

Related databases like data mart and data lake may come alongside DWH. Each have their own distinct functions.

Data mart: A subset of the data warehouse, data mart usually contains one subject area for one department. For example, varieties of vegetables imported to the vegetable section of a general store. The data mart is of two types, dependent and independent, each with its own benefits. The dependent data mart relies on DWH for information, and it maintains consistency. While more strong, dependent data marts are expensive to develop because of dependence on DWH. Independent data marts access information from relevant data sources. It is like a mini DWH. They carry increased risks, as the data comes from various sources and may be inconsistent. However, if you compile independent data marts with discipline, they can subsequently be combined into a DWH.

Data lake: It is usually worked upon cheap and scalable commodity hardware. It is beneficial as the unwanted data can directly be dumped into the lake. While DWH typically consists of texts and numbers, data lakes hold a variety of data such as images, sensor data and social media.

Data mining

The data warehouse also enables data mining. The primary purpose of data mining is to identify and obtain patterns in large sets of data. Consequently, it will help in obtaining relationships between data categories and their underlying business functions.

Such relationships provide workable insights to managers, which further act as antidotes to enhance desired business outcomes such as trends in sales and marketing campaigns and customer growth.
Hence, it is vital to understand DWH as a modern manager. I hope you got a gist about data warehouse and related databases after reading this.

Data Analytics for the App Generation

Data Analytics Everything we do today, we do through apps. We order food through FoodPanda, transportation through Uber, vacation stays through Airbnb, and restaurant bookings through OpenTable. When you wake up each morning, it’s highly likely that the first thing you reach for is an app.

The app-first era we live in has given us an unparalleled opportunity. So many of our customers’s activities are now captured digitally. As business owners, we can learn so much about their preferences from their digital actions, in order to serve them better.

Data Analytics

But this opportunity comes with an equally big challenge. There’s simply too much data generated by these activities.

While BI has been around for 60 years, the real power of data can only be unlocked by companies with huge resources. Even companies with existing BI capabilities struggle to compete in today’s app economy, as their data is locked up in silos — multiple tools that are glued together — which adds communication overheads to their team.

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The Winners and Losers of the App Era

To understand this concretely, it helps to take a look at the winners and losers of this data-rich era.

Some companies are thriving in the age of apps:

  • Amazon has built rich BI tools (some of which are publicly available), and are able to dynamically price goods in its store. This means it continually exerts pricing pressure on its competitors.
  • Airbnb built internal BI tools (one of which is publicly available) in order to onboard new properties and verify new users at scale. On the marketplace side of things, Airbnb is able to dynamically price and sort listings, in order to increase customer satisfaction.
  • Lyft built its own data discovery and metadata management engine, in order to enable its operations people to get the data they need. This way, they don’t bottleneck on a data team.
  • Asana has built its own internal BI tools, designed to deliver insights to as many managers in their organisation as possible.

On the flip side of this, other companies are falling behind:

  • Walmart is unable to keep up with Amazon’s dynamism.
  • The old hotel chains are being disrupted from the bottom by Airbnb, and from the top by the travel aggregators.
  • Taxi companies aren’t able to do supply-demand matching in various locations and are being outcompeted by Uber, Lyft and Grab.
  • And the project management software space has become so competitive that older incumbents like Jira are fighting to keep up.

What is common among these data-driven, operationally savvy app generation companies?

  • First, their tools empower managers at the edges. Instead of relying on a central data team to crunch numbers and hand reports over to business leaders, they have teams that build those tools so business people can get the numbers they need and generate the reports they want by themselves, whenever they want. This is a very different take on a typical data team’s role.
  • Their tools are self-serve from the start — that is, they are designed to be used by non-technical people in any part of the organisation.
  • Their tools are built for governance. With decentralised access to insights, teams need to guard access to their data, be compliant with local regulations, and track metadata — the knowledge around their data. There is also more risk that metric definitions are misunderstood (what is revenue and how is it calculated?) when people communicate across different departments of the company.

It’s no accident that these companies have built their own tools. The old way of doing BI — data teams using silo-ed data tools, serving business users — is simply too slow for this new world!

The truth is that in order to compete in this app generation, you’ll need a BI approach that:

  • Empowers your users. You shouldn’t be always waiting on an analyst in a data team to give you the numbers you need.
  • Is truly self-service. Dashboards for every operational team is a start, but the best companies in the new digital economy give their teams the ability to ask ad-hoc questions of their data, without access to a technically savvy employee.
  • Is secure. With great decentralisation comes great governance challenges. You’ll need to be able to track access to data across your entire organisation, and you need to ensure that you’re fully compliant with local regulations. This should — ideally — be built into your tools for you, so that you never have to worry about it explicitly.

The bad news is that the vast majority of BI tools aren’t built around these principles. If they were, companies like Amazon, Airbnb, and Lyft wouldn’t need to build custom tools for their own use.

But this is changing. This is where we come in.

The good news is that you don’t have to build your own internal tools today! A new generation of BI tools are emerging — tools that are built from the ground up around these principles. Holistics is one of them.

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How Holistics Helps You

Holistics is a modern approach to analytics software. It helps your company build your data foundations and grow your capabilities from scratch. You can automatically shape your raw data into datasets, so you can easily get insights and/or dashboards from your source data.Data

With Holistics, you purchase a data warehouse, connect Holistics to it, and manage every aspect of your data operations in Holistics itself. That includes data governance, access control, reporting, data transformation and ad-hoc queries.

There’s zero learning curve for both data teams and data consumers. Data teams can build data models without learning a new proprietary modeling language. Data consumers can have access to any permutation of datasets from their database in real time, which they can extract and visualize through a pivot table user interface.

The best thing about it? It takes less than an hour to be productive in Holistics — compared to the weeks or months required to build data infrastructure the old way. Over the past three years, we’ve helped hundreds of customers build their data capabilities — in some cases, from absolute zero — with minimal investment and minimal fuss. The Holistics platform runs your data operations … so you can focus on your core business. We see data as a prerequisite to compete in the new digital economy, and we want to make it accessible for everyone. Learn more about us here, or talk to us for a demo today.