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Data, Information & Insights Drive High-Tech Supply Chain Decisions

As awareness of the value of data-driven decision making grows, companies in every sector of the global economy are pushing to gain a competitive advantage through analytical means. In the high-tech electronics industry, where product life cycles are shrinking and time to market is critical, this trend may be even more marked.

Forrester reported 74% of firms say they want to be “data-driven,” with 40% having already invested $10 million or more into their data management and analytics technology–yet only 29% report that they are actually successful at connecting analytics to action. Even gloomier, Gartner projects by 2020, only 50% will have successfully created a narrative that links financial objectives to business intelligence and analytics initiatives. 

The limitless potential of big data combined with the complexity of today’s supply chains is pushing organizations to find new ways to gain an advantage in a digitized world. However, having access to an abundant amount of raw, granular data is not the answer. Whether terabytes or petabytes, the amount is less the issue than how the data is processed, contextualized, and visualized.

Over the coming weeks, we will break down what it means to be “data-driven”, why so many businesses struggle with connecting analytics to action, and how to prime your organization to effectively work with big data. 

Big Data: The Dream

The promise of Big Data is certainly a dreamy one: a never ending accumulation of all types of data, automatically consolidated and processed to expose detailed, timely business insights not visible before; a source of intelligence that can discover hidden roadblocks, streamline supply chains, optimize spending, and even help develop better products, services and business models. It would enable firms to monitor market signals with most sensitivity, and act on them with more agility than what is humanly possible. As Carly Fiorina, former CEO of HP, put it:  “The goal is to turn data into information, and information into insight.” 

It is a vision that is easy to sell to executives. What has been harder to communicate is the work that is required to get there. The majority of businesses are failing to derive true value and insight from analytics because they’ve glossed over the steps and effort needed to make sense of all that data. Buying a new analytics software is not the end-all solution. Effectively harnessing the power of data requires coordinated, strategic enterprise initiative to invest in proven technologies, update workforce skills, and instill a culture of fact-based decision making at all levels.

Defining some buzzwords  

Despite similarities, “data”, “information”, and “insight” are not interchangeable terms. They have all become catch-all buzzwords to describe the Big Data Dream, but it’s critical to know the differences between these key analytical terms: 

  • Data : This is the raw, unstructured facts that are captured before processing. Data is in computer-friendly formats, and is often confined to databases and spreadsheets.
  • Information : The result of collecting, preparing and processing the raw data which can be used to better understand the thing being measured. Information is often aggregated and organized into human-friendly formats such as data visualizations and dashboards. They can often answer simple questions like “What happened?” and “How many, how often, where, when?”
  • Insights : Context-rich results of analyzing information which helps businesses understand a particular situation or phenomenon. Both data and information are required for the discovery of insights. While not all insights are actionable, they are the most active form of data that is tightly linked to business levers.

For example, from a sourcing and supply chain perspective, we can look at everything on a bill of materials (BOM) as data: which components are used and how many, the price per unit, the distributors, etc. When looked as a standalone data set, these facts are fairly useless beyond providing an overview of a product’s parts.

However, once that data is put into context, we see more enhanced views of the supply chain as information: who the cheapest suppliers are, which vendors offer the fastest delivery, which parts are being duplicated across the entire product line, etc. 

Once we enrich the information with external data sets such as the weather, the global market, and social media, we can start to distill insights that can direct real business actions: An upcoming snowstorm requires an extra week for critical deliveries, which can inform schedule changes or alternate vendors. An unexpected decline in a component’s global demand gives your procurement team more negotiation power as opposed to simply re-using the same purchase order (PO). If the customers are unhappy with a specific feature of a product, it can inform the next design iteration and start the procurement process earlier.

As you can see, there is a very defined hierarchy to data, information, and insight. Many analytics solutions and software packages are often just offering more data, which doesn’t drive business insights or actionable business decisions. Being “data-driven” isn’t just having a foundation of data in place, but rather being able to generate, connect, and analyze actionable insights that are relevant to your business goals.

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