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#CPG: 10 Reasons Why Now Is the Time to Get into #BigData

Rick Delgado is a new technologies freelance writer. In this article he takes an objective view of Big Data and lays out 10 reasons why now is the time for Big Data. My observations are italicized below.

1. It will keep your data secure

Being able to completely map your data out in front of you will make you better at analyzing potential threats. You can easily ensure that the most sensitive data you have in your system has all of the necessary security features it needs to stay well protected and within any sort of regulations that your business has to adhere to.
Many BI tools have provided for this sitting on top of the data – however – the big data structure allows this to be mapped into the clusters where data lives.

2. It opens up brand new revenue sources

You will gather a ton of insight into your part of the market. This insight is valuable, and not just to you. Selling out non-personalized industry data about trends in the market can bring in a ton of money from any other businesses operating in the same portion of the industry that you are, making it a great way to bring in some more funds.
While this is not always possible for a CPG company, for example, to do when retailer-sourced data is in the mix there are other opportunities at the enterprise level.

3. It’ll give you an advantage

Businesses are built on tradition. Any industry is going to have years of tradition that believe that one way of doing things is the right way and if it isn’t broke, don’t fix it. One important value that big data has is that it lets you really examine and analyze preconceived notions about aspects of a business you may have never even considered. It will provide better data when it comes to experimenting and trying to innovate, meaning that you’ll be able to get a much better advantage over your competitors.

4. Better Visuals

Big data requires cutting edge data visualizing tools in order to translate all of those numbers and data points into something a bit more tangible. This will increase overall usability that can be utilized by people in the business themselves or their end users.
There are increasing numbers of tools available for the user to create insight from their data lake – Vortisieze prefers MicroStrategy but ultimately is agnostic toward the BI tool selected giving you maximum flexibility.

5. It’s very easy to set up

Big data used to be something that only big corporations could afford to get into. Luckily, there are alternatives that can bring the benefits of big data to a company of any size. Hadoop is a great open source framework that can handle large amounts of data. Hadoop works with Excel, which most businesses already use.
The entry point is such, especially with the ABC of business (Analytics, Big Data, Cloud Computing), that retailer sales/support offices can easily add this as a budget item without involving their IT division.

6. It helps your business evolve

One of the best things about big data is just how scalable it is. Getting the right tools means being able to convert not just number data, but also text, audio, and video files to find patterns that can give a business a ton of insight into the market. Investing in big data as a small company means you’ll be able to easily handle all the data easily that comes with growth.
You begin seeing your business as your customers (the ones buying your products from the retailer) see your business.

7. It can significantly cut back on maintenance

Businesses that rely on large pieces of equipment often spend tons of money when it comes to replacing and maintaining them using predetermined data that tries to predict how long they’re going to remain efficient. Big data allows you to get rid of rounded averages and replace them with specifics, allowing you to squeeze even more life out of machinery that still has plenty of use left.
And with cloud computing, also called data-as-a-service, there is no equipment involved to maintain.

8. It brings you closer with your customers

The customers of today are a lot different than in the past. The rise of the internet allows them to thoroughly and tirelessly research a product before buying and to communicate with tons of people about which brand they should do business with. Big data allows you to better profile these fickle consumers and figure out specifically what they want.
See comments to #6 above.

9. It’s easier to analyze risk

There’s more to business than running your own company effectively. Every business is just one in a large industry that must regularly compete and innovate in order to stay ahead of the rest. Utilizing big data to analyze things like news articles and social media will give you cutting edge information about the biggest and latest developments in your industry.

10. You’ll be able to improve your products

Big data is a key that’ll explain specifically what your consumers really think about your product. Big data is even being used in medical research for companies that do personalized medicine or companion diagnostics, and need to analyze large amounts of biological data. You’ll be able to use your insight to easily get a better picture of your customers based out of different geographic areas and belonging to different demographic groups. Once you’ve seen how the product is perceived, you can easily raise efficiencies in key areas along the product process.
These are extremely valuable insights to present to your retailer buyer – enabling your company to rise above the noise from your competition.

 

Source:  10 Reasons Why Now Is the Time to Get into Big Data

#CPG

#CPGMarketing

#BigData

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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

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Ten Secrets to Win with #Analytics

BI-chalkboard

Nearly every organization today uses analytics. But not every organization is getting as much out of its analytics as it could. So, how do you truly excel with analytics to deliver the best support for decisions?

  1. Don’t fail to plan:  Doesn’t sound like a secret at all does it? Well, too feworganizations have spent the time to begin with the end in mind. The most successful companies always begin their analytics projects with a clear vision of what is the target. The key stakeholders should be aligned by writing down and sharing:
    • What you’re trying to achieve
    • Who you’re trying to reach
    • Why it matters
    • How you’ll measure success
  2. Use your analytics tool to uncover data quality issues:  Don’t let the desire for perfect data be the barrier to very good data. Instead, use your analytics tools to spot abnormalities in your data and learn from them. Then, work with the people who own the data and share your insights; thus helping them fix their processes. By forming partnerships, you can significantly improve your data quality over time.
  3. Use Good Design:  Most of your data consumers visualize data to understand it, so aesthetics play an important role. Like an interior decorator, a good designer can help you develop an intuitive and effective user experience and a great look and feel for dashboards and visualizations. However, data visualization best practices always outrank aesthetic design – every time.
  4. Repetition, repetition & repetition – learn through play & through doing:  Your worst data model is your first one – nobody creates a perfect model for their data on the first try. And that’s OK. Truth is, looking at your data from different angles can teach you a lot about it. Let everyone connect with the data in their own ways — you’ll be amazed at what they discover. Use what they do to inform your strategy (back to #1).
  5. Be your loudest evangelist!  Some software projects are mandatory for users, however, adoption of analytics is voluntary in most organizations. So, if you want people to know you have built a better mouse trap, act like Guy Kawasaki and start promoting it. Recruit your marketing department and sell the value of analytics throughout your entire team and organization.
  6. You need a champion:  champion-awardFind an influential person or team that has an unmet need and empower them with analytics. This can turn them into true believers by showing them what’s possible. Then turn the spotlight on their success to prove the value of analytics to the rest of your business.
  7. Build a Cross Functional Team:  Selling analytics is simple when it becomes easy to repeat successes and avoid failures. Bring together a cross-functional team and put them in charge of:
    • Deciding the role of analytics
    • Defining the standards and tools
    • Identifying best practices and gaps
    • Iterating and improving the solution over time
  8. Have dual processes:  Changing the method of measuring KPIs or profits requires taking your time and getting it right. However, sometimes you have a unique and urgent situation and must develop an app right now to analyze it. Put in place different processes for both scenarios — and accept the fact that it’s OK to build temporary throw-away apps for one‑off projects.
  9. Reports are so 90’s:  Don’t be like most BI deployments that tend to focus on delivering the same out-of-date reports that has been around for decades. Simply describing the situation presented in the data does not provide analytic value for decision makers. You must answer the ‘why?’, not just the ‘what?’. So, shift your efforts to emphasize diagnostic discovery and exploration capabilities.
  10. What is your data worth?  Are you sitting on the proverbial goldmine with your information? Would outside organizations (internal or external to your BI-dollar-signcompany) pay good money to gain access to your proprietary data? Or, as some large retailers do, can you use it to add value for your customers or vendors? Take a step back and see the forest – think creatively about all the ways you could monetize the data you already own.

 

#Analytics

#BusinessAnalytics

#BusinessIntelligence