Talk Data to Me: Data Visualization Best Practices

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Data Visualization Best Practices

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Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derive

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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




You are your password: How #MobileIdentity management hopes to replace your first dog’s name

“Mother’s maiden name”
“Name of your first pet”
“Favorite restaurant”

Those are but a few of the security questions or reminders that help us keep track of passwords across multiple sites and applications. Online service, like Evernote, that sync across multiple devices have stored countless username/password combinations to keep our digital society moving.

However, passwords will be long forgotten – rather than temporarily so – by the end of the decade, said one tech exec who is pushing for a new approach to passwords.

Hugh Owen, vice president of product marketing at MicroStrategy, said, “[Mobile identity management] is going to be very mainstream in a very short period of time. It will be interesting how long it is – maybe four years, five years – before we look back and remember upon the time where we used to rely on passwords and had to remember them all.”

His company is helping to push mobile identity management through wearables, what many security experts hope is the magic bullet for the long outdated mother’s maiden name, “123456” or string of random characters scrawled on a sticky note. Mobile identity management is any service that makes use of pre-verified mobile devices, like smartphones or smartwatches, to send authentication keys, temporary tokens or expiring passwords to provide access to some secure system.

Research increasingly shows traditional authentication hurdles are easily duped and more trouble than they’re worth, and enterprises would do well to look for alternatives both internally and client-facing.

Last month hackers used background information amassed from different cyberattacks and social engineering efforts to dupe the “Get Transcript” application on, siphoning nearly $50 million in illegally requested tax returns in the process. Take that into account with the research Google released in May that shows traditional authentication means do not even help users – only 60 percent of users remembered the answers to questions like “What is your favorite food?” – and make it more likely for cyber criminals to correctly guess questions with popular answers. Some people think mobile identity management could be the answer in the enterprise.

For that reason, MicroStrategy continues to develop its mobile identity management app Usher, a version of which launched with the Apple Watch. Owen said that the idea is nothing new, but it will be the heavy adoption of wearables – which 451 Research contends is already underway with the release of the Apple Watch – that will help take the complex back-end process of identity management and simplify it to a few taps of a smartwatch face.

With Usher’s newest features, organizations can use an Apple Watch to access office locations, devices, business systems and more, and validate identities and discover nearby users. Owen said the new Apple Watch capabilities are more conducive to the at-a-glance workflows of the average user, and he’s always impressed by the creative ways client’s utilize Usher’s software development kits to extend the platform to functions they need.

“We’ve seen some really interesting implementations of it in terms of banking where people are using Usher as the method of second-factor authentication for both giving people access to systems and also approving transactions. It’s integrated in such a way that it’s just part of the workflow,” Owen said.

He also mentioned some programs the company set up at the Saudi Arabia Ministry of Foreign Affairs and Georgetown University, the latter of which is conducting an ongoing “smart campus” pilot that allows students access to buildings and online accounts through the app as opposed to a physical key card.

Some competitors, namely oneID and Authy, also seek to attract an enterprise audience, but MicroStrategy seems to be gaining notable traction. For instance, Apple specifically mentioned its partnership with MicroStrategy’s enterprise efforts in its most recent earnings call, a rare name drop for the normally taciturn Cupertino company.

For the enterprise, mobile identity management seems like a no-brainer. The technology is there; all that’s needed is the implementation. This could be the killer use case that brings smartwatches out of the C-suite and into the hands (on to the wrists?) of the average user.


Source:  You are your password: How mobile identity management hopes to replace your first dog’s name


#DataBlending, #BigData and #BusinessIntelligence

There are many new buzz words buzzing around these days. Terms such as data blending, advanced analytics, big data, cloud computing and business intelligence. Sometimes it is easier to learn a foreign language than to decipher the meaning behind these terms.

As a CPG sales analyst or category manager what is your primary purpose? Increase your brand’s revenue and grow your real estate on the modular. To do so, you aim to create and maintain a competitive advantage with your buyer over your competitors.

Making better and timelier decisions help to gain and maximize any advantage. Drop or increase price, introduce a new product now or six months from now, which audience should be targeted. These decisions, made with the buyer, rely on accurate data and analytics to produce insights – thus business intelligence.

Business intelligence and analytic solutions address these challenges by allowing for easier information discovery and analysis, making it possible for decision-makers at all levels of an organization to more easily access, understand, analyze, collaborate, and act on information; anytime and anywhere.

Remembering your school days and the teacher helping you mix blue paint with yellow to make green paint – the fundamentals of data blending is the same; take one piece of data, mix it with another, and create something different and more appropriate – a new output for analysis and evaluation. This provides new ways to extract value from multiple data sets, discovering patterns in the data, demonstrating more insight to inform the decision-making process.

A few short years ago this activity would have taken sales and business analysts weeks to do this. In short – it would not have been done and if it had the data was sufficiently aged so immediate action was impossible. Today, Vortisieze is blending weather data with POS, inventory and even store straits to discover new patterns that may result in actionable insights.

Working closely with industry leaders such as MicroStrategy and Cloudera we are creating data blending and advanced analytics solutions. Armed with these solutions, we are able to recommend and implement the right data analysis process to meet your business need. Harnessing the power of data blending and advanced analytics means that CPG companies can unlock the insights gained from business intelligence.

Looking over the landscape of business intelligence and analytics solutions on the market it is obvious that the field is crowded, but not all have combined the new technologies with the retail expertise necessary to be right for your business and the objectives you want to achieve. At Vortisieze, because our founders have decades of combined experience in all levels of retail BI – IT, category management and analytics – we understand your business, not just what you do but also how you do it. By collaborating with you we then understand your current and future goals. Vortisieze is able to assist you in identifying the most appropriate of the solutions available to meet your strategic goals.

For more information about Vortisieze and the ABC of business (Analytics, Big Data and Cloud Computing) please contact us today.





#MicroStrategy 10 Adds New #Hadoop #Analytics Capabilities

The leader in the BI analytics platform sphere, and the preferred BI tool by Vortisieze, MicroStrategy is now allowing users to enjoy the richness of data that Hadoop allows.

  • MicroStrategy Inc.’s has launched a major upgrade to its namesake business intelligence platform that promises to help organizations make more out of the vast troves of data they’re storing in Hadoop. That’s done through improved connector bundled natively into the new release.
  • MicroStrategy 10 enables analysts to manipulate imported information using new search and visualization functionality that the company says removes the need to use external tools for that job. That avoids the overhead associated with moving files back and forth, which can amount to significant time savings at the petabyte scale in which many production-grade Hadoop clusters operate.
  • The company promises to take even more delay out of the equation with optimizations to its multi-dimensional analytic algorithms, which now makes it possible to put more data into the logical constructs used to group similar metrics for faster queries. MicroStrategy said that the update has helped PayPal, Inc. achieve sub-second response times for 400-gigabyte workloads.
  • Analysts can take advantage of that performance through the improved interface also introduced in the new release, which is extended to mobile devices with a new companion app that can track the the performance of each user. The client also includes a badge-based security system that allows administrators to control who can access what and how.

Source:  MicroStrategy 10 brings new analytic capabilities to the Hadoop table





#CPGMarketing: The Age of #DataObsession

This new era of big data (some say huge data) is being called “The Age of Data Obsession.” Below are a few snippets from the recent Sogeti / Ovum Business Intelligence & Information Management Symposium 2015.

  • Information is a fundamental currency of business created from the raw material of data, and while in recent years that resource has become abundant almost beyond belief, our ability to benefit from this insight has lagged behind. Organizations must therefore scale up their information factory, accelerate their business processes, and become obsessive about applying data to all aspects of their decision-making, or risk becoming noncompetitive and irrelevant in the modern data-driven era.

This is clearly a call to action or fall behind and out of the game.

  • For best-in-class businesses, the obsession with data entails its application to every aspect of their operations, from the optimization of business processes, through to decision support for more complex strategic problems. It involves interrogating as wide a range of sources as possible, both internal and external, structured and unstructured; and of delivering the resulting insight at a speed which can match and ultimately accelerate those operational and decision-making processes. Increasingly this requires real-time or near real-time data analysis and presentation, which has significant implications for the data to insight process.
  • Manipulating data at this speed and scale requires a fundamental change in approach, both in terms of technology, and of the underlying data management processes that are applied. From a platform perspective, we are currently seeing the integration of solutions specialized for particular aspects of data handling and manipulation into a unified architecture, reflecting the need to combine existing data warehouse investments with newer big data systems and analytic databases. Logically however, the change in data analysis goes much further, with a growing design mind-set that mandates a large data lake to which organization is applied only at the point of seeking to answer a question (driven in part by the rapid adoption of in-memory computing).

This new obsession blows away the capabilities of traditional relational database management systems (RDBMS) and is tailor-made for what big data (primarily using Hadoop) can achieve. This is true both in the capability to incorporate unstructured data and rapidly load, process and present that data in insightful ways.
Combined with BI analytics tools harnessing the new technology this data brings power to the decision maker.

  • There is a big caveat here though: however powerful the technology, if the organization does not also upgrade its foundational data management processes and its information management strategy to match, then the outcome will at best be indiscriminate and at worst chaotic. From a strategy perspective, organizations must define their information needs and capabilities in support of business objectives, while data management processes must be sufficiently robust so that insight can be generated from data in a systematic and repeatable fashion.

Source: Sogeti / Ovum Business Intelligence & Information Management Symposium 2015




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.




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.