Business analytics is the process — or practice — of using quantitative, objective methods to extract meaning from data. Companies that use analytics effectively can transform data into business insight, making it easier to make informed business decisions.

By employing the latest tools, stacks, models, and techniques, business analytics can identify new patterns, shed light on complex situations, and make it easier for teams to consider all available options, predict outcomes, and present critical risks and opportunities to business decision-makers.

A data analytics stack is an integrated system composed of individual tools that perform distinct functions. The three basic functions of a data stack include the data pipeline, data warehouse, and data visualization.

The data pipeline collects, extracts, transforms and loads the data to the data warehouse, where the data is stored. From there, it’s easy to organize, edit, and analyze the data and move to the final stage, data visualization. Data visualization is where near-real-time custom reporting happens and where graphical representations of a company’s data are realized.

What’s the value of an analytics stack?

To succeed in a highly competitive market, modern business needs to think of themselves as a data business first. And it appears as though more and more are: TechRepublic reported that businesses — even small businesses — are increasingly using analytics to survive the challenges they’re facing as a result of the pandemic. Researchers found that 49% of companies are using data analytics “more or much more” than before the COVID-19 crisis.

Because the value of data depreciates over time, using it quickly — and creating speedy (but accurate) reports — is a valuable feature of a good business analytics stack. Here are a few more ways a great analytics stack supports business success.

Gain Business Agility

An effective analytics stack helps companies be more agile, decisive, and forward-thinking. It helps them create a data-first mentality so they can leverage information and increase revenue through gained insights.

Make Better Decisions

A greater volume, variety, and cleanliness of data sources make it easier for business leaders to make smarter, quicker decisions.

Anticipate the Road Ahead

A business that gains insight from its data is better prepared to identify shifts in consumer/business behavior that can affect its business model — and respond accordingly to them.

Improve Efficiency

Thanks to artificial intelligence (AI), an employee’s time is split between system and stack maintenance on the backend and generating actionable reports for stakeholders on the frontend

Create New Business Opportunities and Revenue Streams

Gaining insight from data can be a goldmine, and some companies have found great success selling data to other businesses.

Move New Ideas Forward, Faster

Business leaders who rely on business analytics to support new proposals or initiatives can more easily answer questions like: what kind of data will new initiatives generate? How will that data be incorporated into current reporting? Does this data hold any external value?

But wait, there’s more

Business analytics can also help companies:

  • Find new customers and increase customer retention.
  • Enhance customer service.
  • Increase social media interaction and engagement.
  • Improve accuracy of sales predictions
  • Respond to trends.
  • Improve focus.
  • Develop more impactful marketing efforts.
  • Gain a better understanding of performance and improve processes.

How Can Companies Lay the Groundwork for Data Analytics?

Finding a robust analytics stack that unifies all operational and business data sources — and offers access to all kinds of different data — is the key to laying the groundwork for leveraging AI capabilities within a business. Discovering the stack offering a gold-standard in reporting, and one that supports stakeholders’ Business Intelligence (BI) requirements, can propel a business forward.

Often, the journey to the perfect stack starts with the tools a company is are already using! Raw data consumed or generated by the business is stored in its native format, like an operational database, NetSuite, Dynamics 365, or Salesforce. Then, the data is pushed into a data lake (which is also known as a data warehouse) where it’s cleaned, compartmentalized, and prepped for consumption through an extract, transform, load (ETL) or extract, load, transform (ELT) solution.

From there, BI tools like Power BI, Tableau, or Looker ingest the data and deliver accurate reporting and metrics.

It sounds like a lot, but a trusted business and technology transformation partner can help companies select the appropriate tools for their stack, adopt a data management/governance strategy that supports business needs, and help ensure they’re using each tool appropriately and to capacity. These partnerships often save companies time and money in the long-run.

When Is It Time For a New Analytics Stack?

Knowing when it’s time to address inefficiencies, discrepancies, and reporting issues can be tough. After all, when you work with something every day, it’s easy to overlook — or at the very least, become accustomed to — its inadequacies and flaws.

If any of the following challenges sound familiar, it’s time to assess analytics processes and solutions to determine where improvements can be made:

  • Reporting frequency is not timely.
  • Ad-hoc reporting requests negatively impact scheduled reports.
  • Stakeholders acknowledge inaccuracies in their reporting and lack of access to necessary data.
  • BI users and analysts spend more time acquiring, understanding, and cleaning data, than creating useful reports.
  • Little is known about existing data – no insights into data lineage, no metadata records, no central repository of data (data-wiki).
  • Engineers must manually move data between layers or manually trigger ETL/ELT jobs for proper data consumption.
  • Raw data sources are used in reporting.
  • Data cleansing or data archiving activities are not performed before reporting.
  • An active data warehouse doesn’t exist and no data acquisition and cleansing activities are performed.
  • Historical data is not available for reporting; either through lack of access or no historical data being kept.

How MorganFranklin Can Help

MorganFranklin can help companies assess their current analytics stack and create a remediation strategy for pain points and create an analytics roadmap to help inform analytics strategy and decisions. Our business and technology transformation experts can also help companies find the right vendor or tool for their current — and future —  analytics needs. Then, they can help implement an entire analytics stack or support the integration of individual components like data warehousing, BI tools, or ETL tools.

MorganFranklin has expertise in redesigning and designing all-new stacks, improving reporting cycles to reduce inefficiencies, exploring data and data architecture, and providing recommendations for automation and improving workflows. Contact us today to get the conversation started.

Authored by: Pierre Stricker

Talk to our experts.