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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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 data cleansing strategy


Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

data cleansing strategy  and identify quality issues; data cleansing , to correct and standardize data in preparation for consumption by the user community; and data monitoring , to control quality over time. Diagnose with Data Profiling By creating data profiles, organizations can gain an understanding of quality issues that stem from their business data. Metrics are created based on selected fields to measure the quality of data represented in those fields. Examples of data profiling metrics include the following: Number of

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

Process Manufacturing (ERP)

The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as order processing and production scheduling. ERP began as a term used to describe a sophisticated and integrated software system used for manufacturing. In its simplest sense, ERP systems create interactive environments designed to help companies manage and analyze the business processes associated with manufacturing goods, such as inventory control, order taking, accounting, and much more. Although this basic definition still holds true for ERP systems, today its definition is expanding. Today's leading ERP systems group all traditional company management functions (finance, sales, manufacturing, human resources) and include, with varying degrees of acceptance and skill, many solutions that were formerly considered peripheral (product data management (PDM), warehouse management, manufacturing execution system (MES), reporting, etc.). While during the last few years the functional perimeter of ERP systems began an expansion into its adjacent markets, such as supply chain management (SCM), customer relationship management (CRM), business intelligence/data warehousing, and e-Business, the focus of this knowledge base is mainly on the traditional ERP realms of finance, materials planning, and human resources. The old adage is "Such a beginning, such an end", and, consequently, many ERP systems' failures could be traced back to a bad software selection. The foundation of any ERP implementation must be a proper exercise of aligning customers' IT technology with their business strategy, and subsequent software selection. This is the perfect time to create the business case and energize the entire organization towards the vision sharing and a buy in, both being the Key Success Factors (KSFs). Yet, these steps are very often neglected despite the amount of expert literature and articles that emphasize their importance.    

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Documents related to » data cleansing strategy

Scalable Data Quality: A Seven-step Plan for Any Size Organization


Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

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Data Quality: Cost or Profit?


Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.

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A Road Map to Data Migration Success


Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project teams encounter some very specific management and technical challenges. Minimizing the risk of these tricky migrations requires effective planning and scoping. Read up on the issues unique to data migration projects, and find out how to best approach them.

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Six Steps to Manage Data Quality with SQL Server Integration Services


Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

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Appliance Power: Crunching Data Warehousing Workloads Faster and Cheaper than Ever


Appliances are taking up permanent residence in the data warehouse (DW). The reason: they are preconfigured, support quick deployment, and accelerate online analytical processing (OLAP) queries against large, multidimensional data sets. Discover the core criteria you should use to evaluate DW appliances, including performance, functionality, flexibility, scalability, manageability, integration, and extensibility.

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Transactional Data: Driving Real-Time Business


A global survey of IT leaders shows that most organizations find it challenging to convert high volumes of fresh transactional data into knowledge that business users can efficiently access, understand, and act on. SAP and HP are tackling this challenge head-on. Download this article to learn more.

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Big Data, Little Data, and Everything in Between—IBM SPSS Solutions Help You Bring Analytics to Everyone


Regardless of the type or scale of business data your users need to harness and analyze, they need a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end. Fortunately, IBM SPSS solutions provide just such an ecosystem that can make different kinds of data stores—from Hadoop to those proverbial spreadsheets—useful sources of business insight and decision support.

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Tailoring SAP Data Management for Companies of All Sizes


The need for accurate data management such as upload or download of data between a company’s data sources and SAP systems is more critical than ever. Users are relying on manual operations, which are inherently error-prone, and time- and resource-intensive. Today's environment requires enterprise-class automation to overcome these challenges of data management. Learn about one solution that can help improve SAP data management.

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Containerized Power and Cooling Modules for Data Centers


Standardized, pre-assembled, and integrated data center facility power and cooling modules are at least 60% faster to deploy, and provide a first cost savings of 13% or more compared with traditional data center power and cooling infrastructure. Facility modules, also referred to in the data center industry as containerized power and cooling plants, allow data center designers to shift their thinking from a customized “construction” mentality to a standardized “site integration” mentality. This white paper compares the cost of both scenarios, presents the advantages and disadvantages of each, and identifies which environments can best leverage the facility module approach.

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Forrester TechRadar Report: Data Security


Data security is not just an IT issue these days but a business imperative, as data volumes explode and it is becoming a Herculean task to protect sensitive data from cybercriminals and prevent privacy infringements. As data volumes continue to rise, the burden of protecting sensitive data and preventing security breaches can be crushing. It is necessary to take a holistic, comprehensive, and long-lasting approach to data security that encompasses people, processes, and technology.

This Forrester TechRadar Data Security report provides a framework for developing a long-term approach to keeping your organization’s information secure. Data breaches and insider threats are becoming more common, and your organization needs to achieve compliance and secure privacy without affecting the bottom line. Most companies are also interested in adopting cloud, mobile, and other technologies, which can complicate data security matters even more.

This comprehensive and in-depth report evaluates 20 of the key traditional and emerging data security technologies. To make the report, Forrester interviewed over 40 experts, customers, and users, and drew from a wealth of analyst experience, insight, and research.

Use this report to get informed about what you need to consider to restrict and strictly enforce access control to data, monitor, and identify abnormal patterns of network or user behavior, block exfiltration of sensitive data, and render successful theft of data harmless.

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