How To Get Business Insights From Your Data (Only 3 Ways)

Data is the lifeblood of organizations, but what do companies actually do with all the data they have curated?

Companies can only extract insights to get business value from data in three ways:

1. Analytics

The goal is to organize all of your data into a centralized location to power insights and dashboards. Business leaders need data at their fingertips to make better strategic decisions.

2. Process Automation

The goal is to save time by automating manual tasks and business processes. Instead of manually copying information from one system to another, it should take place automatically.

3. Product Development

The goal is to turn information into valuable data products that clients can purchase. These could be insights, automated workflows, or raw data feeds for monetization.

By understanding the stakeholders, objectives, and market landscape for each bucket separately, it becomes easier to identify opportunities to use data to generate value within the organization.

Data Analytics

1. Stakeholders: Head of Business Intelligence, Head of Analytics, Data Analyst.

2. Business Objective: Analytics teams are primarily responsible for generating dashboards and insights to answer key business questions. The goal is to create a suite of dashboards, reports, and alerts that are accessible to leaders and business associates across the company.

3. Example Deliverables: KPI dashboards, sales funnel conversions reports, multi-touch attribution analyses, product adoption scorecards, etc.

4. Tooling: Most modern analytics teams today use a combination of an ELT Tool to replicate data into a data warehouse, a data transformation tool to manage the conversion of raw data into valuable insights, a data warehouse to run the data processing, and data visualization tool to generate dashboards and insights.

Some teams are even starting to blur the line between analytics and process automation with reverse ETL.

Process Automation

1. Stakeholders: Head Of Business Operations, Head Of Process Automation, Head Of Marketing Operations.

2. Business Objective: While analytics teams focus on using data to inform strategy and measure success, process automation teams focus on streamlining the execution along the way with automation. Instead of relying on a marketer to manually move data from a CRM system to an Email Service Provider (ESP) for a campaign, operations teams set up data pipelines for the information to automatically move from system to system

3. Example Deliverables: Automated customer onboarding experiences, automated marketing campaign operations, automated employee onboarding processes, streamlined invoice to cash processes, and B2B lead generation.

4. Tooling: Process automation teams want to streamline workflows, and the approach to-date has been to use an Integration Platform as a Service (iPaaS) tool, or a Customer Data Platform (CDP) to move data from point to point.

The critical consideration for automating workflows is 'Am I able to pull data from a particular source and deliver it to the particular destination to power this specific workflow'.

As ELT tools and Reverse ETL tools add more connectivity capabilities, we are confident that big data management with ETL for warehouse process automation is the future.

Product Development

1. Stakeholders: Head of Product, CTO, VP of Engineering, Head of Data Operations

2. Business Objective: Product and engineering teams focus on generating value for external clients, not internal stakeholders.

They build production data pipelines that ingest client or third party data and curate the information into value.
This value could be the result of off-the-shelf analyses that can be sold to clients, or pipelines that push data to downstream systems to help clients automate business workflows in a simpler way.
Another path to create value is to monetize data directly by selling curated or raw data to third parties.

3. Example Deliverables: Self-service analyses for clients, off-the-shelf pipelines for business workflows, raw data feeds or APIs for clients to purchase data.

4. Tooling: Product and engineering teams build capabilities that other companies in the market don't already have.

In most scenarios this involves custom data connectors, unique transformations, and highly customized visualization layers.

For tooling, these teams lean on big data processing engines, distributed systems architectures, and cutting edge frontend development frameworks to get the job done.

Summary

With a clear framework for how to leverage data within the enterprise, you can now more easily identify opportunities to use data to generate value

Portable turns products into connected solutions. Teams combine our no-code integrations with existing products and services to power analytics and automate workflows. We can load data into warehouses, extract data out of warehouses, and connect business applications together directly.