• Keith Meyer

The Roadmap to Modernizing Analytics

We’ve developed a proven methodology to help industry-leading organizations modernize their analytics. It starts with a capability / maturity assessment where we help your organization envision a future state and its associated benefits. Roadmaps generally include four important steps: Data Ingestion, Data Curation, Premium Analytic Content, and End-user Enablement.

Data Ingestion

Multi-source analytics environments can be created in hours or days, not months or years.

We find that the highest value analytics occurs when you blend data from different sources, as different sources typically represent different parts of an overall business process and putting that data together enables insights that cannot otherwise be identified. Data Ingestion is the process of copying data from many sources into a single environment for analytics.

In the past, systems and tools used an Extract Transform Load (ETL) approach for Data Integration, meaning that purpose-built data transformations occurred in the process of copying data to the target. This required that all of the uses for the data needed to be defined before the project could start. Practitioners took this approach because the database resources for compute and storage were quite finite. Today we take an Extract Load Transform (ELT) approach, which helps us start projects faster and at the same time handle the breath of requirements that are identified over time.

ELT is enabled by Cloud Native tools like Fivetran for ingesting a broad range of 3rd party data sources such as Salesforce, Workday, NetSuite, SAP and many others, as well as tools like S3, Glue, Data Pipeline and Kinesis for file-based, query based, or streaming data sources. Modern cloud database environments, such as Redshift and Snowflake can scale dynamically and cost effectively so there is no need to pre-define, constrain or otherwise over-engineer analytics environments. Multi-source analytics environments can be created in hours or days, not months or years.

Data Curation

Once data from the various source systems is flowing into a scalable cloud database, it needs to be organized for end-user consumption. We refer to this organizing step as data curation. Data curation includes connecting data keys across sources, interpreting data fields, records, or combinations of data into business concepts that would be recognized by an end user, summarizing at reportable levels of granularity, standardizing field names, documenting data lineage and codifying standard ways of interpreting data. In the past this was done with different tools for coding, code management and documentation. Today, we have a modern tool that puts all the pieces together in DBT.

Premium Analytic Content

Now that all the data is in one place, our teams of data engineers, data scientists and data visualization experts generate premium analytic content to provide the most impactful analysis of the most important business processes. This comes out in the form of compelling, engaging works of data visualization art that tell your organizations story. Premium analytic content helps to set the bar for what’s possible.

In many cases, there are opportunities to leverage Machine Learning/AI to go beyond telling the story but predicting or optimizing the story. Here our expertise in tools like Data Robot and SageMaker, as well as our expertise in statistical methods and rules-based algorithms help customers leverage analytics for guided and automated decision making.

End-User Enablement

Cleartelligence is your full-service partner for modernizing analytics for digital transformation. We help you establish a vision and strategy, create a data analytics environment, and use leading edge tools to create premium analytic content. Additionally, we provide training and day-to-day enablement and support to people in your organization to create a data driven culture.