Tag Archives: Business

A Tale of Two Disciplines: Data Scientist and Business Analyst

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data scientist and BA

The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

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Supply Chain & Big Data ÷ Analytics = Innovation

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Google the term “advanced analytics” and you get back nearly 23 million results in less than a second.

Clearly, the use of advanced analytics is one of the hottest topics in the business press these days and is certainly top of mind among supply chain managers.

Yet, not everyone is in agreement as to just what the term means or how to deploy advanced analytics to maximum advantage.

At HP, the Strategic Planning and Modeling team has been utilizing advanced operational analytics for some 30 years to solve business problems requiring innovative approaches.

Over that time, the team has developed significant supply chain innovations such as postponement and award winning approaches to product design and product portfolio management.

Based on conversations we have with colleagues, business partners and customers at HP, three questions come up regularly – all of which this article will seek to address.

  1. What is the difference between advanced and commodity analytics?
  2. How do I drive innovation with advanced analytics?
  3. How do I set up an advanced analytics team and get started using it in my supply chain?

Advanced analytics vs. commodity analytics

So, what exactly is the difference between advanced analytics and commodity analytics? According to Bill Franks, author of “Taming The Big Data Tidal Wave,” the aim of commodity analytics is “to improve over where you’d end up without any model at all, a commodity modeling process stops when something good enough is found.”

Another definition of commodity analytics is “that which can be done with commonly available tools without any specialized knowledge of data analytics.”

The vast majority of what is being done in Excel spreadsheets throughout the analytics realm is commodity analytics.

Read more at Supply Chain & Big Data ÷ Analytics = Innovation

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What’s the Difference Between Business Intelligence (BI) and EPM?

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Business Intelligence Emerges From Decision Support

Although there were some earlier usages, business intelligence (BI) as it’s understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s. Then in 1989, Howard Dresner (a former Gartner analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” In fact, Mr. Dresner is often referred to as the “father of BI.” (I’m still trying to identify and locate the “mother of BI” to get the full story.)

The more modern definition provided by Wikipedia describes BI as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes.” To put it more plainly, BI is mainly a set of tools or a platform focused on information delivery and typically driven by the information technology (IT) department. The term “business intelligence” is still used today, although it’s often paired with the term “business analytics,” which I’ll talk about in a minute.

Along Came Enterprise Performance Management

In the early 1990s, the term “business performance management” started to emerge and was strongly associated with the balanced scorecard methodology. The IT industry more readily embraced the concept around 2003, and this eventually morphed into the term “enterprise performance management” (EPM), which according to Gartner “is the process of monitoring performance across the enterprise with the goal of improving business performance.” The term is often used synonymously with corporate performance management (CPM), business performance management (BPM), and financial performance management (FPM).

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Why Supply Chains Need Business Intelligence

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Companies that want to effectively manage their supply chain must invest in business intelligence (BI) software, according to a recent Aberdeen Group survey of supply chain professionals. Survey respondents reported the main issues that drive BI initiatives include increased global operations complexity; lack of visibility into the supply chain; a need to improve top-line revenue; and increased exposure to risk in the supply chain. Fluctuating fuel costs, import/export restrictions and challenges, and thin profit margins are driving the need for businesses to clearly understand all the factors that affect their bottom line.

Business Intelligence essentially means converting the sea of data into knowledge for effective business use. Organizations have huge operational data that can be used for trend analysis and business strategies. To operate more efficiently, increase revenues, and foster collaboration among trading partners companies should implement BI software that illuminates the meaning behind the data.

There is a vast amount of data to collect and track within a supply chain, such as transportation costs, repair costs, key performance indicators on suppliers and carriers, and maintenance trends. Being able to drill down into this information to perform analysis and observe historical trends gives companies the game-changing information they need to transform their business.

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Overcoming 5 Major Supply Chain Challenges with Big Data Analytics

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Big data analytics can help increase visibility and provide deeper insights into the supply chain. Leveraging big data, supply chain organizations can improve the way they respond to volatile demand or supply chain risk–and reduce concerns related to the issues.

Sixty-four percent of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations (Source: SCM World). Ninety-seven percent of supply chain executives report having an understanding of how big data analytics can benefit their supply chain. But, only 17 percent report having already implemented analytics in one or more supply chain functions (Source: Accenture).

Even if your organization is among the 83 percent who have yet to leverage big data analytics for supply chain management, you’re probably at least aware that mastering big data analytics will be a key enabler for supply chain and procurement executives in the years to come.

Big data enables you to quickly model massive volumes of structured and unstructured data from multiple sources. For supply chain management, this can help increase visibility and provide deeper insights into the entire supply chain. Leveraging big data, your supply chain organizations can improve your response to volatile demand or supply chain risk, for example, and reduce the concerns related to the issue at hand. It will also be crucial for you to evolve your role from transactional facilitator to trusted business advisor.

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How Big Data And Analytics Are Transforming Supply Chain Management

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Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing.

Of course supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionizing industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.

This was clearly a situation that couldn’t last. Many factors can clearly impact on supply chain management – from weather to the condition of vehicles and machinery, and so recently executives in the field have thought long and hard about how this could be harnessed to drive efficiencies.

In 2013 the Journal of Business Logistics published a white paper calling for “crucial” research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken, and it now appears many of the concepts are being embraced wholeheartedly.

Applications for analysis of unstructured data has already been found in inventory management, forecasting, and transportation logistics. In warehouses, digital cameras are routinely used to monitor stock levels and the messy, unstructured data provides alerts when restocking is needed.

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How to recover from supply chain disruptions

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Risk mitigation is a crucial component of supply chain management. Preparing for potential disruptions is one of the most important yet challenging tasks faced by company managers, especially since there is an abundance of possible situations threatening operations at all times.

Unfortunately, damage control planning is something many companies tend to neglect. Last year, a study conducted by the supply chain management team at the University of Tennessee found that only about 50 percent of businesses have a recovery process in place to reference in the event a facility’s operations are interrupted.

Importance of response planning
Companies of all sizes are susceptible to dangerous disruptions, with global supply chains being the most vulnerable. Which is why it is surprising that the report also discovered nearly all, or 90 percent, of surveyed organizations do not take potential risks into consideration when outsourcing.

It’s understandable that managers are generally more focused on improving day-to-day operations, such as customer service, identifying cost-savings opportunities and driving revenue. However, disruptions along the supply chain have the power to severely impact financial growth and overall performance.

Between natural disasters, security breaches, safety and regulatory compliance and system failures, it is virtually impossible to anticipate what will be affected and when attacks may occur. But the best approach for supply chain teams to take is implementing strategic risk management practices that will help minimize monetary losses associated with disasters.

Read more at How to recover from supply chain disruptions

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Poor Visibility Puts a Majority of Organizations at Risk for Supply Chain Disruption

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The majority of companies that experienced a supply chain disruption in the last year cited either a tier 1 or tier 2 supplier as the predominant source of the disruption, according to 2015 Supply Chain Resilience Report from the Business Continuity Institute and Zurich Insurance. Half of all respondents in the report cited a tier 1 supplier, the immediate or direct supplier, as the major source of the supply chain disruption and an additional 21% cited their tier 2 supplier, the supplier of the OEM’s tier 1 supplier.

The report also showed the majority (72%) of organizations lack full visibility into their supply chains. What is troublesome, too, is that nearly 1 in 10 (9%) of the more than 500 companies surveyed for the report do not fully know who their key suppliers are. This can no doubt make supply chain risk management even more difficult for firms that lack proper oversight on who exactly their suppliers are.

According to Thomas Kase, vice president of research at Spend Matters and an expert on supply chain risk, sometimes companies lack quality visibility and have a fragmented picture of their suppliers and what they deliver.
“The end result is a foggy mess,” Thomas said.

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Big Data: The Latest Rage in Supply Chain Management

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Early uses of big data were concentrated in two areas: customer segmentation/marketing effectiveness, and financial services, particularly in trading. Recently, supply chain has become the “next big thing.”

Why? A company’s supply chain is rich with data, and it’s also a large cost component. Combined, those facts mean that advanced analytics can become a strategic weapon for optimizing the supply chain.

However, many companies can’t see the forest for the trees. They are optimizing, but not strategically. When applying data to supply chain, it’s critical to step back and look at what truly drives business value.

“They’re Digging in the Wrong Place”

As every fan of “Raiders of the Lost Ark” knows, Indiana Jones found the Ark of the Covenant first. The Germans had far greater manpower and resources and they were more efficient, but they were competently digging a hole in the wrong place. The same goes for using big data in supply chain optimization. You could have the most efficient process in the world, but if you’re making the wrong amount of the wrong product, it will hurt your business.

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How visibility can drive supply chain performance

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How visibility can drive supply chain performance

At its heart, supply chain management requires a balancing of operational efficiency, customer satisfaction and quality. Managing the true cost to serve each and every order is the aspiration to allow better negotiation and value creation across the supply chain. Customer and consumer centricity helps anticipate product and service requirements. But supply chains are becoming more extended and complex with a consequent increase in risk and the need for resilience. There are multiple data sources making it difficult to manage and measure end-to-end processes and metrics. Aligning priorities through integrated planning remains pivotal but there is an explosion of data available that needs to be incorporated and the value extracted to understand how supply-demand issues impact profit and revenue targets.

Organisations are looking to enable better and more consistent decision-making across complex processes with diverse systems and data. Many are leveraging business intelligence (BI) platforms to give them the capability to make decisions across the organisation, including environments where mobility and access to decision-critical information on the go is crucial. Putting the information in the hands of the people on the front line – those managing supply chain processes – is key to enabling decision making at the point of decision. But this requires synchronising an enormous amount of data that comes from many systems and sources in a way that it can be easily consumed by people who need to act on the insights.

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