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

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).

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

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Supply Chain Management in QlikView / Qlik Sense

With complicated structure in a supply chain, it has been a challenge for executives to see and understand the associated changes and movements in a supply chain. Examples are raw materials, inventory, products, marketing campaigns, promotions, and other supply chain activities.

As brands and products proliferate, are spun off, and re-consolidated, supply chain companies find themselves struggling to understand what they have, what they need, and where they’re going. Doing so requires a tremendous amount of data, drawn from both external sources (suppliers, partners, customers) and internal ones (marketers, production managers, supply chain groups). The ability to see all of the data surrounding a brand at a glance is a tall order, one only made harder by the proliferation of systems and processes designed to support it. Before companies can profit from efficiencies of scale, they need to consolidate these systems. This is an area where business intelligence (BI) can help them.

However, melding disparate data sources through business intelligence turns out to be a disaster, when companies are using multiple technologies under one roof. These technologies could be from Microsoft, Oracle, SAP, IBM, Teradata, and others. People are struggling before BI implementation, and people are struggling even more after it. As a result, instead of placing information in the hands of the managers who needed it, they are now locked inside those data and technologies, where they could barely get to the real BI they desperately need. On the other hand, IT departments are struggling with questions like: “how many people I needed to build reports”, “how long it is going to build reports”, and “what those reports should look like”.

To meet the challenges of data and technologies, a possible solution is QlikView or Qlik Sense from Qlik. Compared to other BI vendors, the most unique feature of QlikView / Qlik Sense is that people don’t have to think about the joins of tables; people don’t even have to think about which tables to pull out of their ERP. The appliance just bolts onto the side and sucks the whole thing out. People, or even non-IT people, can spent a week extracting the relevant data tables from the central data warehouse, then loading them into QlikView / Qlik Sense as individual data sets — one for sales, one for materials management, and so forth. And, suddenly, they can gaze across a total landscape of its supply chain before drilling down by product or brand or segment or market — or any combination it liked.

With QlikView / Qlik Sense, companies can train or hire a handful of savvy managers who in time became the trainers for their respective divisions. When the need arises for a report, they’ll point you to an existing report or enhance it or build a new one if need be, if everyone agrees it’s the right thing to do. People are taking reports into their own hands and customizing them to suit their needs.

In addition to this special feature, people can also implement their supply chain management BI by using one of the following templates in Qlik Demo site:

  • Executive Insights
  • Production Insights
  • Forecasting and Planning
  • Sourcing and Supplier
  • Regulatory Compliance
  • IT Management
  • Warehousing and Distribution
  • Transportation and Logistics
  • Merchandise Management

In addition, people can find other supply chain solutions provided by Qlik vendors at the Qlik Market. A screenshot of the Order and Inventory Management Dashboard is enclosed below. You can go to its interactive demo site here.

Order and Inventory Management.qvw

In summary, supply chain management is implemented in QlikView / Qlik Sense as applications or reports in all areas of the supply chain management, which can come from one of a reports template Qlik provides, custom made to match what you have today, or created by one of its vendors. The applications and reports do not need specialized IT departments to create and can be created by your very own people in the field.

References:

 

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How Do You Turn Supply Chain Data into Actionable Information?

How Do You Turn Supply Chain Data into Actionable Information?

There is a continuum in terms of presentation of data that allows for continuous sophistication in understanding and interpreting data. There are lots of ways to view data, but three that are particularly useful in supply-chain analytics are –Reporting, Scorecarding, and Benchmarking.

The simplest form of looking at data is what we have all seen dozens of times, we call it “Reporting”. Back in the day, reporting was numbers printed out on green bar paper, but today’s business intelligence reports are far more detailed and dynamic than in the past. For instance, a BI report of today displays all the data about transportation providers as usable information, in a scorecard format. Factors such as on-time delivery, freight cost per unit shipped, and transit time are assigned metrics and weighted averages to help users determine how well carriers are performing overall.

Operation managers and executives who want a quick, daily overview of what is happening in their transportation or supply chain network use dashboards to provide information in near real-time to help users understand what is happening within their network, and allows them to make proactive decisions to remedy problems as they occur. Where reporting is really like looking in the rearview mirror, dashboards are used to see what’s going on now, and makes it easier for users to identify trends and exceptions, and to intervene before something goes wrong.

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Hadoop and Data Warehouses

Hadoop and Data Warehouses

I see a lot of confusion when it comes to Hadoop and its role in a data warehouse solution.  Hadoop should not be a replacement for a data warehouse, but rather should augment/complement a data warehouse.  Hadoop and a data warehouse will often work together in a single information supply chain: Hadoop excels in handling raw, unstructured and complex data with vast programming flexibility; Data warehouses, on the other hand, manage structured data, integrating subject areas and providing interactive performance through BI tools.

There are three main use cases for Hadoop with a data warehouse, with the above picture an example of use case 3:

1. Archiving data warehouse data to Hadoop (move)
Hadoop as cold storage/Long Term Raw Data Archiving:
– So don’t need to buy bigger PDW or SAN or tape

2. Exporting relational data to Hadoop (copy)
Hadoop as backup/DR, analysis, cloud use:
– Export conformed dimensions to compare incoming raw data with what is already in PDW
– Can use dimensions against older fact table
– Sending validated relational data to Hadoop
– Hadoop data to WASB and have that used by other tools/products (i.e. Cloud ML Studio)
– Incremental Hadoop load / report

3. Importing Hadoop data into data warehouse (copy)
Hadoop as staging area:
– Great for real-time data, social networks, sensor data, log data, automated data, RFID data (ambient data)
– Where you can capture the data and only pass the relevant data to PDW
– Can do processing of the data as it sits in Hadoop (clean it, aggregate it, transform it)
– Some processing is better done on Hadoop instead of SSIS
– Way to keep staging data
– Long-term raw data archiving on cheap storage that is online all the time (instead of tape) – great if need to keep the data for legal reasons
– Others can do analysis on it and later pull it into data warehouse if find something useful

Thanks for reading this article. If you have any opinions, please leave a comment below or send us a message

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The TOFU (Top of Funnel Users) Approach to Business Intelligence

The TOFU (Top of Funnel Users) Approach to Business Intelligence

An interesting article in Forbes.com entitled, “Why Top Of The Funnel BI Will Drive The Next Wave Of Adoption”, written by Dan Woods, sparked some great conversations about bottom of the funnel users (20-30% wanting specific business information), and Top of Funnel Users (or TOFU) that want to interact with information in a personalized way and express their interests. I was fortunate to have Matt Milella, Director of Product Development for Oracle Business Intelligence Mobile Apps, and Jacques Vigeant, Product Strategy Director for Oracle Business Intelligence & Enterprise Performance Management, join me for a podcast to discuss their opinions about “The TOFU approach to business intelligence (BI)”.

Jacques explained that the article is basically about how BI has historically focused on what we refer to as the ‘business analyst’ or the ‘power user’. That’s the person in a company that has the unenviable task of analyzing data, finding trends, and synthesizing data into dashboards that he/she then shares with management. The common thinking, in BI companies, is that roughly 20% of the users prepare data that the ‘rest of us’ consume. There are many practical and technical reasons why BI started using this model 30 years ago, but the world of technology has come a long way since then. Today, the average user can do much more with much less help from IT.

Do you think that this article is interesting? Do you have any opinions? Thank you for reading. If you have any questions, send us a messageor leave a comment below.

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FusionOps Unveils LiveAnalytics for Supply Chain Data

FusionOps Unveils LiveAnalytics for Supply Chain Data

FusionOps, a supply chain analytics company that provides a cloud-based business intelligence (BI) application, has launched LiveAnalytics for supply chain data. LiveAnalytics uses images and live metrics to create infographics for supply chain processes and workflows.

The FusionOps application allows businesses to create new analytics from scratch. In addition, the application offers thousands of configurable analytics, metrics and tickers, FusionOps said.

LiveAnalytics leverages FusionOps’ interactive, root-cause analysis across the supply chain. FusionOps said LiveAnalytics users can visualize changes in their supply chains in real-time and evaluate data from all functional areas to become more efficient.

Some of LiveAnalytics’ features include:

  1. Alerts – When alerts are triggered, users are notified via email about supply chain events in real-time.
  2. Key performance indicator (KPI) dictionary – The new KPI dictionary explains pre-built and company-specific metrics.
  3. Personalized navigation – Users can access thousands of dashboards, KPIs and reports directly from LiveAnalytics’ main navigation and “Favorites” menus.

Thanks for reading this article. Welcome to leave a comment below or send us a message if you have any opinions.

 

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New Approaches to Analytics to Revolutionize Logistics

New Approaches to Analytics to Revolutionize Logistics

Three stages are commonly used to categorize an organizations maturity in their use of business intelligence and analytics technologies:

  1. Descriptive: What happened in the past?
  2. Predictive: What will (probably) happen in the future?
  3. Prescriptive: What should we do to change the future?

Descriptive analytics typically means good old fashioned business intelligence (BI) – reports and dashboards.  But, there is a newish technology in the Descriptive category – one that I might argue is worthy of a category in its own right.  That technology is visual data discovery.  The visual data discovery approach has a rapidly growing fan base for many reasons, but one stands out:  It increases the probability that business managers will find the information they need in time to influence their decisions.

Visual data discovery tools typically provide:

  1. Unrestricted navigation through, and exploration of, data.
  2. Rich data visualization so that information can be comprehended rapidly.
  3. The ability to introduce new data sources into an analysis to expand it further.

By helping to answer a different class of question – the unanticipated one – visual data discovery tools increase the probability that managers will find the information they need in time to influence their decisions.  And that, after all, is the only valid reason for investing in business intelligence solutions.

If you have any opinions, you are welcome to leave a comment or send us message.

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Bywaters waste management uses BI to improve customers’ recycling

Bywaters waste management uses BI to improve customers’ recycling

Bywaters, a recycling and waste management company, has improved productivity by 4% using Pentaho data integration and business intelligence software.

Sasha Korniak, head of analytics and data science at Bywaters, masterminded the project at the family-owned company, which operates nationally, and includes Nandos, Guy’s and St. Thomas’ Hospital, and BNP Paribas among its 2,000-plus customers.

“I wanted Bywaters to embrace a data-driven culture that would give authority and confidence to make autonomous decisions substantiated by credible data and enable consumers to increase recycling and sustainability,” says Korniak.

“We are no longer just a waste management company, we are a waste consultancy, improving our customers’ recycling through providing the data”, says Korniak. “If you are not data driven, but just go out and collect bins, the sustainability of your business will be damaged”.

If you have any opinions, leave a comment below or send us a message.

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iView Systems’ iTrak® Business Intelligence Delivers Dynamic Dashboard Risk Analytics & Reporting

iView Systems’ iTrak® Business Intelligence Delivers Dynamic Dashboard Risk Analytics & Reporting

iView Systems, a leading provider of loss prevention solutions for the security and surveillance environment, is excited to announce the most recent addition to the iTrak® family of Incident Reporting and Risk Management solutions, the iTrak® BI (Business Intelligence) Module. The iTrak® BI Module delivers powerful dashboard visualizations from information reported in the iTrak Incident Reporting and Risk Management and other data sources in real-time, providing users a visual representation of their incident and other iTrak information. This allows organizations to quickly extract meaningful business intelligence to detect emerging trends & identify risks, threats & vulnerabilities.

iTrak® BI real-time, interactive dashboard reporting and visualization.

  1. Manages dynamic business data, providing the ability to control the visualization and analysis of data in real-time.
  2. iTrak® BI is equipped with a large selection of high-quality data controls and visualizations, effectively presenting the data to associated audience.
  3. Connects and consolidates data into one system, regardless of where your data resides; saving time and money.
  4. iTrak® BI empowers end-users to create, interpret, analyze and drill down through a wealth of information for effective decision-making in real time.
  5. iTrak® BI adapts to the business so users don’t have to adapt to the product.
  6. iTrak® BI gives users a range of viewing options that are designed specifically for both desktop and mobile delivery providing important metrics on the-go.
  7. iTrak® BI allows communication, collaboration and the ability to take direct action via commenting capability directly on the dashboards – allowing effective and immediate the insight to make better business decisions.
  8. iTrak® BI Dashboards lets users choose, filter, format and sort metrics they need to see, with the ability to share and collaborate the finished results (mashups) with other users.
  9. The web-based solution lets users create, view, and interact with dashboards directly in a web browser – with no need to install a separate desktop application.

If you have any question, leave us comments below of send us a message.

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Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

 Magic Quadrant for Business Intelligence and Analytics Platforms

Data discovery capabilities are dominating new purchasing  requirements, even for larger deployments, as alternatives to traditional BI tools. But “governed data discovery” — the ability to meet the dual demands of enterprise IT and business users — remains a challenge unmet by any one vendor.

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see “Extend Your Portfolio of Analytics Capabilities”) in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.

For this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 17 capabilities across three categories: information delivery, analysis and integration.

As a result of the market dynamics discussed above, the capability definitions in this year’s Magic Quadrant have been modified with the following additions and subtractions to reflect our current view of critical capabilities for BI and analytics platforms.
Capabilities dropped:

  1. Scorecard: Most companies do not implement true scorecard/strategy maps using BI platforms — they implement dashboards. Also, most BI vendors report limited sales activity for their scorecard products. Scorecards are primarily delivered by corporate performance management (CPM) vendors (see “Strategic CPM as a Driver for Organizational Performance Management”). Therefore, we have included scorecards as a type of dashboard, rather than as a separate category.
  2. Predictive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”
  3. Prescriptive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”

Capabilities added:

  1. Geospatial and location intelligence (see the Analysis section)
  2. Embedded advanced analytics (see the Analysis section)
  3. Business user data mashup and modeling (see the Integration section)
  4. Embeddable analytics (see the Integration section)
  5. Support for big data sources (see the Integration section)

Feel free to leave us your comments or send us a message.

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