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Article

Reduce manual work, support business analytics with data process automation

July 11, 2024 · Authored by Loren Den Herder

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Alteryx, Tableau, and Power BI are types of data process automation tools — or more specifically, analytic process automation (APA) platforms. These tools were developed to empower business users to create their own solutions to problems that traditionally cannot be accomplished with existing systems.

Understanding business process automation tools can be complicated, but the importance of data analytics to inform decision making is as important as ever. With the right tool for your organization, you can turn data into meaningful and useful information to help drive business decisions.

This article provides an overview of:

  • Who can benefit from the tools
  • What the benefits are
  • What processes can be automated
  • Governing with process automation tools
  • Three implementation phases

Who should use business process automation?

Your organization may benefit from using these platforms if:

  • You’re a data focused company aiming to upskill its workforce
  • You have complicated periodic — annual, monthly, weekly — processes that require creating a big spreadsheet
  • You want to be more data driven but are finding the learning curve is steep and the foundation needs improvement
  • Your processes are maintained or done by a limited number of individuals and there’s a risk that the process would be difficult to reproduce in the event of a staffing change
  • You want to free up time for employees who are wrangling Excel sheets
  • You want to complete time-intensive analysis more frequently and efficiently without having to adjust head count

What are benefits of business process automation tools?

Baker Tilly Professional

Loren Den Herder

Managing Director

The information provided here is of a general nature and is not intended to address the specific circumstances of any individual or entity. In specific circumstances, the services of a professional should be sought. Tax information, if any, contained in this communication was not intended or written to be used by any person for the purpose of avoiding penalties, nor should such information be construed as an opinion upon which any person may rely. The intended recipients of this communication and any attachments are not subject to any limitation on the disclosure of the tax treatment or tax structure of any transaction or matter that is the subject of this communication and any attachments.

APA systems are valuable for data preparation and the blending of data sources, which as you know, is what takes up most data scientists’ time. Similarly, multiple departments and knowledge users also face these types of problems when collecting and representing data.

While data automation provides an easy way for employees to convert large or complex files into a digestible format, it also reduces manual errors. By removing that barrier to data collection and combination, your company can get results, and higher return on investment (ROI), faster.

Additional benefits

Automation tools can provide your organization with a variety of benefits, including:

  • Reduce or eliminate spreadsheet babysitting
  • Custom solutions to Extract-Transform-Load (ETL) challenges using a wide range of data ingestion, a robust pallet of specialized tools, and a business user friendly configuration
  • Built in scenario and exception reporting within analysis for a better control environment and audit trail for critical accounting or operational decision points
  • Translate complicated processes into custom workflows, reducing time in preparing analysis while increasing reliability in a consistent process
  • Funnel prepared data into a diverse set of analytic tools, including predictive, machine learning, geospatial, and more

Automate manual processes

Data scientists spend a large portion of their time collecting data sets, cleaning and organizing data, mining data for patterns, and refining algorithms.

Many business processes can be automated, helping to make data manipulation easier. Below are examples of manual tasks that can be streamlined with business process automation.

Your specific use cases will depend on your organization as well as your unique financial planning and analysis (FP&A) and automation needs.

FP&A
  • What-if analysis in forecasting
  • Reconciliation of consolidation and financial system
  • Capital budgeting
  • Document key performance indicators (KPIs) and glossary
  • Effectiveness of price increases
  • Reforecast days sales outstanding
Accounting and operations
  • Automate monthly accounting close
  • Automate daily reconciliation
  • Inventory and obsolescence processing
  • Reconciliation of fixed assets
  • Reconciliation of consolidation and financial system
Tax and audit
  • Automate tax credit calculations
  • Sales and use tax apportionment and compliance
  • Align continuous audit approach
  • Fraud detection automation
  • Transfer pricing
  • Property taxes
  • Depreciation reporting
  • Identify non-taxed transactions
  • Customs duty drawback
  • R&D tax credit calculations
  • Automate daily reconciliations
Business analytics
  • Demand planning
  • Customer segmentation
  • Hardware and software utilization
  • Supply chain logistical effectiveness
  • Sales planning
  • Automation of management reporting
  • Capital expenditures and resource planning
  • Real estate planning
Payroll
  • Variable and incentive payout
  • Identify profit centers with inaccurate day sales outstanding (DSO) metrics
  • Validate employee payroll tax accuracy
  • Management of multiple pay cycles
  • Standardized reporting to enable time and attendance verification

Governing with business process automation

APA tools enable employees to focus on the analysis and strategy aspects of the data, rather than organizing it. It empowers all employees, not just experts or upper management, to use data to drive decisions. It also aligns department and company goals with one consistent process, promoting efficiency and coherence across the organization.

Organizational models

Governing with APA tools involves the application of different organizational models to manage data analytics.

  • Centralized model. Skilled IT professionals, analysts, and data scientists manage a centralized analytics function. This model is designed to bolster strong data governance and eliminate redundancy, although it may slow time to value and inadvertently encourage rogue analytics.
  • Decentralized model. This empowers everyday knowledge workers to analyze data and share capabilities. This model is designed for scalability but could lead to redundancy and quality issues due to a lack of collaboration and governance.
  • Center of excellence model. This is a hybrid approach that empowers every knowledge worker in collaboration with the center of excellence. This model allows users to share knowledge about data governance, best practices, and technical skills, supporting an agile and nimble organizational strategy.
People

People play a crucial role in governing with process automation tools.

  • Management shares and celebrates problem formulation and solutions
  • Data engineering focuses on the data used and provides opportunities to share or certify sources
  • Data science tracks ROI
  • Legal and privacy administrators prioritize legal and regulatory best practices
  • IT focuses on access control best practices
  • Stakeholders are engaged through demo sessions and on-demand and live classes to learn technology and techniques, and rewards and recognition for analytic delivery encourage employees to upskill and gain certifications

Implementation road map

The process of integrating new technology into an organization's workflow can be broken down into three phases. Integrate these phases with a broader technology road map for your organization.

  1. Assessment phase. Conduct interviews with stakeholders who currently own and manage the processes. Key pain points are identified and process improvement ideas and opportunities are explored, along with available resources.
  2. Design and Build phase. Discuss the results from the assessment phase and establish an action plan for a new report. A to be state is then designed and built, with collaboration on several demonstrations during the build process.
  3. Ongoing Support and Enablement. Discuss potential deployment methods with your team and third-party business analysts. Additional areas of development are explored after completion.

This structured approach helps support a smooth transition, with ongoing support and development opportunities.

Have questions? Baker Tilly can help.

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