CDO’s Guide to Spend Analytics for Manufacturing

Every Chief Data Officer (CDO) knows that accurate spend visibility, and the subsequent analytics makes the difference between a “business as usual” and a “best-in-class” procurement organization.

But without the right spending insights, organizations can’t make smart decisions that lead to continuous improvements and economic advantages. And the obvious question then emerges: “Can we trust any of our existing spend analysis?”

One of the main culprits obfuscating the truth about spending – traditional approaches to data mastering produce less-than-optimal (and traditional) results. The velocity and variety of data is outstripping MDM approaches that are dated, don’t scale, and are highly resource-intensive. Most manufacturers still rely on application integrations to unify data. This may work for some applications, but it is not the solution to every problem. Others aggregate data into lakes and then employ data engineers to write business rules to compensate for data quality and variety. This method falls short as well.

The Future of Data Management: Agile Data Mastering and Classification
The software development industry has been employing agile approaches for years, often referred to as DevOps. The same DevOps practices and principles apply to data management, or DataOps, using Agile Data Mastering (ADM).

ADM connects people, processes and tools together to treat data unification as an iterative process that combines ML with subject-matter expertise. As subject matter experts train and validate the ML models, the models’ accuracy improves. The smarter the model becomes the less human interaction is required.

Here are some of the main benefits of Agile Data Mastering:

1. It’s scalable: With human expertise combined with ML, companies can integrate datasets from a variety of sources and file formats, allowing scale without sacrificing accuracy.

2. It’s faster: ADM tools can deliver results in days that traditional methods might have needed months or even years to obtain. Yes, ERP consolidation projects, for example, should take only a few months and not years.

3. It allows for team innovation: When technical teams and analysts aren’t spending endless days on data prep, they can focus more on unlocking actionable business insights from unified, accurate data.

4. It opens new opportunities: With reduced costs, projects shelved or stalled due to high cost and high risk can finally get the attention they deserve.

5. It promotes flexibility: ADM allows teams to respond to the unexpected, faster and more effectively than ever before, such as switching suppliers due to quality issues, finding alternate/substitute parts or proactively adjusting to rising or falling raw material costs.

Companies like Tamr use ADM to drive analytic outcomes and solve data problems every day. This human-guided, machine learning modern data management model is crucial for enterprises because it unifies data up to 10 times faster than traditional methods at up to 90 percent lower cost.

So how can a business – and more specifically obtain a more accurate analysis of their spending? Tamr created the CDO’s Guide to Spend Analytics for Manufacturing to help answer this question

Inside, we outline how “mastering” spend analytics allows you to:

  • Make better business decisions by cleaning and mastering your data
  • Effectively manage suppliers, spend, and materials
  • Quickly act upon latent data by combining Machine Learning and human expertise

Read the full guide here: