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Business Intelligence Software BuyerView 2014 Highlights

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As a business intelligence software provider, Silvon is always looking for research that helps us better understand the BI market (where it’s been and where it’s headed) as well as what our customers are looking for in a BI solution. We recently read the 2014 BI Software BuyerView report from Software Advice, who researches BI intelligence tools, and found some really interesting highlights regarding the state of the industry.

Here are three key findings from their report:

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Guidelines for Estimating the Payback of a Demand Forecasting Solution

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ROIMost companies that make the decision to move to an automated demand forecasting solution are primarily driven by:

  • Obvious forecast accuracy challenges
  • Highly variable process that requires too much local knowledge of the company’s products – loss of a knowledge worker
  • Lack of time and knowledge to produce the detail level forecasts required to support a more efficient manufacturing or distribution system
  • Downstream inventory problems that are clearly driven by forecasting problems
  • An attempt to drive more cooperation and ownership between sales and operations through a consensus-based forecast

In this post, I’ll describe what you should expect to pay when making an automated forecasting solution investment.  And I’ll offer up some general guidelines for estimating the ROI and payback from bringing such a solution onboard in a manufacturing or distribution business. View Article…

An Evolutionary Approach to BI

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BusinessIntelligencePhased deployment is a strategy that favors an incremental approach to rolling out Business Intelligence (BI) solutions, typically starting with a single department or application within a company. In contrast to enterprise-wide deployment, phased deployment allows early lessons learned to shape future roll-outs and ultimately bring greater visibility and performance management to business users on an enterprise level.

Here’s a proven 5-step strategy to follow when deploying and evolving with BI. View Article…

Assessing the Lifetime Value of Your Customers

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Customer Lifetime ValueCustomer Lifetime Value (CLV) is a critical concept for virtually every organization that’s customer-centric. At a granular level, it helps companies decide which tactics to use for which customer. At a more macro level, it is the key ingredient in calculating customer equity.  Yet, it’s one of the most overlooked and least understood metrics in business — even though it’s one of the easiest to figure out.

Why is this particular number so important? Mainly because it will give you an idea of how much repeat business you can expect from a particular customer, which in turn will help you decide how much you’re willing to spend to “buy” that customer for your business.  Once you know how frequently a customer buys and how much he or she spends, you will better understand how to allocate your resources in terms of customer retention programs and other services you’ll need to not only keep your customers, but keep them happy. View Article…

The Right Processes Will Make or Break Demand Planning

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MakeOrBreakManaging an effective demand planning process is challenging even in a small company and can be the primary source of problems or solutions to many enterprise planning challenges.  Demand planning people are rarely heroes and often villains — becoming the source of everyone’s anger and criticism.  Even so, they are still the go-to people when answers are needed.  In many ways this simply proves that demand planning is one of the key upstream processes in running an efficient intelligent enterprise.

There are several facets to a structured demand planning process that need special attention View Article…

Data Consistency is Key to Analytics

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Data Consistency is Key to AnalyticsWhether it’s “simply” the data that gets generated by the business applications we support or the availability of data from other sources such as partners/distributors or even the brave new world of social media – the availability of data typically isn’t an issue when it comes to BI applications.  The volume of data continues to grow by unprecedented volumes each year.

The quality and ‘usability’ of that data, however, is critical to the success and acceptance of any BI strategy.  Data inconsistency results in misinformation and incorrect decisions. View Article…

Business Intelligence: The “Build vs. Buy” Debate

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Business Intelligence: The “Build vs. Buy” DebateThe debate between building a business intelligence system from the ground up or buying a pre-packaged solution has been going on for years.  Many companies that plan to deploy a BI solution will consider the in-house option first because they perceive an in-house solution can be more easily adapted over time, require no dependency on an external provider, and be less costly and more scalable in scope.

However, there are corresponding points in favor of a packaged BI solution: View Article…

Why Retailers Should Care About Data Mining

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RetailIn an increasingly competitive market space, retailers need to know everything they can about their customers: who they are, what they buy, when they buy, why they buy? And thanks to the amount of data flying around about customer buying behaviors retailers can answer all those questions and more, provided they have the technology needed to collect, organize, clean, and analyze that information. With data mining as part of a business intelligence initiative, retailers can have real answers to real questions in real-time.

Here are 3 reasons why retailers should care about the data mining abilities a business intelligence platform can give them: View Article…

Stratum.Connector Processing Options

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Stratum Under CoversStratum.Connector provides different options for updating the Viewer Analysis Services cube and database with data from the Stratum database. Stratum.Connector V6.0 offered two main processing options. The recently released Stratum.Connector V6.3 introduced a third option. The various processing options were also re-named in V6.3 to better reflect their functionality.

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