An illustration of Microsoft Power BI and Azure Machine Learning being feed data by Azure Synapse Analytics

Feb 23, 2021

  • // Analytics Operations,
  • Business Strategy
  • Nexus Cognitive Chief Executive Officer, Anu Jain
    Anu Jain | CEO

Analytics Framework

Components of an analytics framework.

Last week I talked about the characteristics and advantages of a theoretical analytics framework and why it's so critical to have one to guide your analytics initiative. As important as the theoretical framework is, however, it must be backed up by a sound technical infrastructure. The tools and technologies you choose to implement analytics will largely determine the success of your project. The right combination of tools and technologies will help embed analytics into your corporate DNA and drive better business outcomes.

I'm not going to get deeply technical here, and I'm not going to discuss all the components of the analytics framework — there's simply not enough space. My focus here is solely on that part of the framework that spans data ingestion, preparation, and discovery.

There are basically two types technologies used to capture, manipulate, and deliver data to users: the data warehouse and the data lake. My focus here will be the data lake.

To be sure, the data warehouse is a powerful platform for analytics delivery. However, building data warehouses is logistically and technical complex, costly, and time-consuming. Though there are companies that have gotten it "right" such as WalMart and Amazon, there are also countless stories of data warehousing gone terribly wrong.

I believe that data lakes — for all their documented pitfalls — enable the fastest, most flexible platform for analytics data delivery. Unlike data warehouses, data lakes ingest data — of virtually all types — in its native form, and only upon query by the user is that data formatted for analysis. Data lakes also typically use open-source storage technology such as Hadoop, so the storage costs are greatly reduced.

Data lakes also support traditional technical functionality such as cleansing and governance, and their structure — organization and formatting when the user queries the data, rather than on entry into the data lake — enables flexible, deep analysis using multiple data types simultaneously.

Maybe the most important advantage of a data lake, however, is its speed to deployment. Data lakes — because the data is ingested in its native format, and because the storage is open source — can be developed and deployed much more quickly than a data warehouse — which delivers better business outcomes faster and cheaper.

As I said above, although they deliver powerful results, data lakes really only have three processes: data ingestion, preparation, and discovery.

For data ingestion, most data lake technologies support batch loads of data. However, for near-real-time analysis, what's needed is a data lake product that supports both batch and streaming data ingestion in virtually any format. With this capability, analysts can query the system and get data that has almost zero latency, thus helping them ask and answer questions — and make decisions that have an almost immediate impact on the business.

The preparation process is where native-format data that has been ingested into the data lake is transformed — based on previously set policies and rules — into usable data that can be queried for analysis. Best-in-class data lake technologies help you create rules to standardize and validate data based on your unique business rules and analysis goals — along with any regulations you might have to observe. For instance, an insurance company may need to mask sensitive data such as social-security number, based on user needs and security roles. A top-notch data lake product will also have pre-built transformation algorithms for common data types. This will speed up the transformation process and deliver data into users' hands more quickly.

The data discovery process is where the benefits of a data lake are most apparent. In a data lake, there is an enormous pool of cleansed data, just waiting to be accessed and analyzed. In choosing a data lake technology, you'll want to look for a intuitive GUI that helps users search for the data they want and helps them build queries that return data in a graphics or text-based format that supports sophisticated analysis.

The benefits of a data lake are clear:

  1. They're quick to deploy. Because data lakes use schema on read (which means that data isn't processed until its queried) there isn't a months-long effort to write a rigid schema to organize the data. Instead, data is only organized when users need to access it.
  2. They're flexible and scalable. Because they accept most data types and they're open source to facilitate more storage, data lakes can change as your organization changes, and they can grow as you grow.
  3. They make managing data easy. With powerful cleansing and transformation capabilities facilitated through an easy-to-use interface — as well as pre-built transformation rules — data lakes make data management less complex and more streamlined.
  4. They deliver powerful analysis capabilities. With user-friendly GUIs and self-service data query abilities, users can quickly search for and access the data they need, and ask business-driven, complex questions — driving more informed, quicker decision-making that leads to accelerated outcomes.

I've only begun to scratch the surface of data lake technology here. I'd love to hear from you and get your opinions on data lakes vs. data warehouses. You can contact me directly on Linkedin or at