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Uncover #BigData Quality Issues With Your #Analytics Tool

As Business Intelligence grows in importance within many large and medium-sized organizations there are many issues surrounding the data that an organization has to deal with in order to improve its decision making processes. One of the most important is data quality which is frequently highlighted by Business Intelligence.

Comprehensive management of data quality is a crucial part of any Business Intelligence endeavor. It is important to address all types of data quality issues and come up with an all-in-one solution.

  • A Single (Trusted) Version of the Truth

    • Governing data quality ensures trust in your information, fixing data problems during the extraction, transformation and loading process, and creating policies to know when data is an outlier.
    • VortiSieze software supports the consistent accuracy of complete data so you can focus on making more informed decisions and gain efficiencies in your business processes.
    • Supporting growth, innovation and compliance is based your ability to make crucial business decisions which suffers when you lack credible information.
    • Ensuring a successful data management initiative requires carefully planning for data quality, i.e. accuracy.
    • A carefully planned data quality initiative is essential to any successful data management initiative – be it a business intelligence (BI) or data warehousing (DW) project, a new implementation of a customer relationship management (CRM) system, or a data migration (DM) project.
    • You can be more confident in your business decisions by taking the necessary steps to provide complete and reliable data.
  • Data Cleansing Delivers Data You Can Trust

    • With VortiSieze, parsing, standardizing and cleansing data, from any domain, source or type, is functionality built into the solution.
    • Parsing data identifies individual elements and breaks those into components. These are rearranged into a single field or move may elements from a single field into many, unique fields.
    • Once parsed, your data is check for consistency, preparing for validation, correction, and accurate record matching.
    • Your data is standardized using business rules that defines formatting, abbreviations, acronyms, punctuation, greetings, casing, order, and pattern matching – placing you in control according to your business needs.
    • Dirty data (data with incorrect elements) is cleansed by correcting or adding missing elements and is done on a wide variety of data types
  • Enhancing Data Gives Your Greater Insight and Opportunity

    • You can maximize the value of your data by enhancing data with internal or external sources, i.e. enriching your existing data set by appending additional data to it.
    • This provides a more complete view of your data that can help you, for example, more effectively target customers and prospects, take advantage of cross-selling opportunities, and gain deeper insights into your business.
    • With VortiSieze, enhancement options include:
      • Weather data to predict long term trends in agriculture.
      • Commodity prices to aid in negotiating with a valued distributor or retailer.
      • Planogram or modular data to enhance shelf display planning.
      • Geocoding longitude and latitude information to records for marketing initiatives that are geographically or demographically based.
      • Geospatial assignment of customer addresses for tax jurisdictions, insurance rating territories, and insurance hazards.
  • Uncover Real Issues with Data Input, Matching and Consolidation

    • Consolidate data to uncover hidden relationships and provide a single version of the truth.
    • Incorrect data creates problems that flow ‘downstream’ making it difficult to identify the correct entity to enter new information against and to verify even basic information such as how many customers you have, which products they own, and which products come from which suppliers.
    • Duplicate data presents a myriad of issues and it becomes difficult to:
      • Identify the correct data to key new information against
      • Verify even basic quantitative information on customers, products, or which products come from which suppliers.
    • Duplicate records can exist in more than one source systems; data matching algorithms within VortiSieze can reduce or eliminate duplicate data.
  • Governing Data With Data Quality Measures

    • VortiSieze software helps you to analyze and understand how trustworthy is your enterprise information.
    • You will also get continuous insight into the quality of your data.
  • You Make Better Decisions with Reliable Data That is Trusted

    • VortiSieze empowers you to enhance data quality for effective decision making and business operations.
    • You can easily find data outliers and as these arise correct the issue working to proactively prevent quality issues.
    • With VortiSieze, you can:
      • Define and implement aggressive data policies, continuously assess data quality and repair data problems.
      • Improve data by parsing, standardizing, and cleansing data from any source, domain, or type.
      • Enhance data with internal or external sources to maximize the value of your data.
      • Consolidate data to uncover hidden relationships and provide a single version of the truth.

#BigData

#Analytics

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5 Steps to Plan for Successful #BusinessIntelligence

Business intelligence is gain a lot of attention in successful organizations. And for good reason – there is a correlation between more advanced use of data and a positive impact on bottom-line earnings and business overall performance. It comes down to this – organizations which successfully leverage their data for insight and strategic advantage perform better and move with the market faster than those that do not.
Those groups which embrace technology that allows for data visualization and discovery – correctly –achieve success more often those who don’t at all – or do so incorrectly. Developing a plan for using quality data to their advantage keep these organizations from being left behind. Here are some tips on how to get started:

  1. Have a vision: BI technology is ubiquitous and new advances are made almost weekly. New technologies allow for data visualization and data discovery allows for the exploration of data in intuitive ways. However, that is like saying new darts are as accurate as drone missiles but missing a dartboard. Fundamentally there must be something to aim at – and in BI this comes down to figuring out what questions to ask and work out which data matters the most. Leveraging BI requires an insight into the big picture – one that receives support across all organizational functions and establishes how the organization can successfully evolve with a clear vision.
  2. Business outcomes must be defined: The Cheshire Cat said, “If you don’t know where you are going, any road will get you there.” For your BI project to succeed it is important to set specific and measurable targets. Begin by leveraging a mix of top-down and bottom-up approaches to recognize potential business use cases. Using a top down approach can be used to spot KPIs (Key Performance Indicators) and bottom up approach can be used to determine the data to improve the KPIs. It is advisable to achieve quick wins – use cases which can be improved in a short period of time to lock-in ongoing business support.
  3. Build the team structure: Generally, a lack of skills is one of the main obstacles in building a successful business intelligence team. When a sufficient number of people with the right skillsets are missing, either in organizations, or the marketplace optimal use of data cannot be achieved. A best practice adopted by the leading data revolution companies is to appoint a chief insight officer or a chief data officer to establish actionable insights.
  4. Create a governing group: When you consider implementing a business intelligence solution creating a Center of Excellence comprised of people who understand both – the company’s business and the IT environment – is highly recommended. This team can help build a BI system that is flexible and adaptable, two very important factors if the analytics solution is to stay relevant as the business evolves.
  5. Stress the technology: Many BI tools are architected as hierarchical, top-down data structures. This is easy to organize the data behind the scenes but limits the users to a predefined path required to find the data that is needed. Other tools are associative in nature which allows data to be ‘discovered’ intuitively much the way information is uncovered using, for example, a Google search. Obtain trial versions of both types and have a pilot group (or groups) stress-test these to uncover which approach best suits your needs.

Big data analytics is a big trend in the business world today and for good reason. However, as with everything in business, there is no one-size-fits approach. Every business has unique needs. However, by starting with the end in mind and working backwards, instead of buying-in a system and then adapting your organizational culture to shoe-horn-fit-it-in, you are likely to discover the best way to grow your business with the help of your business intelligence system.

#BusinessIntelligence