January 25th  and 26th  Intetics visited an internationally renowned Big Data Innovation Summit in Las Vegas held by the Innovation Enterprise. The event brought together 200 participants, speakers and world-class delegates to inspire, educate and provide unprecedented access to incredible networking opportunities. The summit is known for its interactive nature, connecting attendees with leading organisations, and offering a unique platform to share and discuss key challenges.

The 2 days’ event brought industry leaders together to discuss data analytics, data management, data security, data science, big data technologies and emerging algorithms.

Keynote speakers included Nicholas Marko Chief Data Officer & Director of Neurosurgical Oncology GEISINGER, who discussed “Actionable Data in Healthcare Trumps Big Data, Every Time”. Reza Rahimi Senior Staff Software Engineer (Office of CTO) HUAWEI TECHNOLOGIES, who is involved in transferring developed technology to Huawei enterprise product line, touched the topic of “Improving Technology through Big Research”.  Yves Bergquist Project Director, Data & Analytics, Entertainment Technology Center UNIVERSITY OF SOUTHERN CALIFORNIA delivered and in-depth discussion on Developing Systems and Processes through Culture.

“The speakers covered a multitude of the most pressing issues and challenges, data scientists face daily,” said Boris Kontsevoi, Intetics President and CEO. “The speakers` line-up left me inspired and with the depth of knowledge to drive Intetics team forward. I left the summit with a baggage of ideas and a bunch of interesting opportunities.”

Big Data Innovation Summit has quite literally travelled across the globe over the last 6 years. Today the event has educated over 15,000 data scientists and analytics executives through presentations, workshops, training sessions, panel discussions and more. The stage plays host to some of the world’s most exciting organisations including Google, Facebook and LinkedIn, and has attracted attendees from a whole host of backgrounds.

*featured image by Innovation enterprise.
Objective

 

To automate collection, analysis and integration of survey information about lottery ticket sales from the lottery operator’s network of retailers.

Challenge

 

A Canadian lottery operator relies strongly on their retail network for sales of lottery tickets. The operator must monitor how intermediaries carry the product; they are required by law to regularly inspect licensed vendors to ensure adherence to regulatory and policy requirements. Their existing process for conducting inspections consisted of semi-manual inspection planning, assignment distribution to local inspectors via e-mail, and semimanual analysis and database entry of collected surveys. This process was time consuming and the client was looking for a technology solution to make inspections more efficient. They also required that the solution kept inspections confidential, allowed fast and safe data transfers, and included simple and reliable procedure of localization and a convenient data format for further processing.

Solution

 

The lottery service provider chose Intetics, because of its successful track record of developing custom software solutions and experience developing inspection applications. Intetics first collected the client’s requirements and built a prototype. Based on the feedback, a plan was formulated and Intetics put together a team of software engineers. Using SCRUM methodology throughout development, Intetics built a desktop and a mobile application. The desktop application was developed using Cordova and .NET. It served as the main platform from which the coordinator selected, analyzed and entered information into the database. The coordinator could also send assignments to inspectors immediately via e-mail. The iOS and Android mobile applications were developed for field inspectors. Once inspectors loaded the app, they used their mobile devices to access their assignments and surveys. The assigned inspections were illustrated on an interactive map with pinned locations of the retail sites. No Internet connection was required to use the map or fill surveys, making offline work possible. The completed surveys were returned to the coordinator via e-mail immediately. The returned surveys were automatically imported into the new desktop application used by the coordinator.

Results

 

The lottery operator received a complete, efficient solution that reduced time spent on collecting and processing information about their retail network. The client was able to start using the system within 5 months of cooperation with Intetics. The application allowed faster transfer of survey information between the coordinator and field inspectors. It eliminated a significant amount of the coordinator’s manual operations. It reduced the time required to analyze results and update the database. Overall, it made the inspection process better organized and easier. Finally, thorough documentation was provided, which will allow the client to easily operate the system on their own.

Big data is everywhere, but how are companies actually using it? Whether you want it to or not, the tech world is transitioning into a data-driven age. With these changes new technologies are taking hold, and companies are finding new and exciting ways to implement ideas and bring innovation to their businesses.

This presentation brings forth the most transformative and pressing ideas for managing big data. It explores how technology transforms business and how data is helping drive the change. The focus is on real-life examples of how companies are implementing location-based services, Internet of Things, and omni-channel systems technologies and what benefits these technologies are bringing.

What we’re going to cover in this presentation:

What is Big Data

  • Where are we now
    • People: profile, skills, shortage?
    • Challenges
  • Use Cases
    • Location Based Services
    • Internet of Things
    • Omni-channel Retail
    • Other uses & tools
  • Tips for you
  • Conclusions & Trends

Download full version of white paper to explore how technology transforms business and how data is helping drive the change.

Big Data & Business Intelligence Development

Big Data Teams to discover new potential

You have the data. We have the data experts. Intetics’ innovative, proven Remote In-Sourcing® approach ensures that a highly qualified team of industry-certified technology specialists work with your data analysts to unlock the value of your Big Data to better understand customer behavior. We’ll develop the Business Intelligence solutions that will grow your business and help you achieve your goals.

Wide Technical Expertise

MS SQL with SSIS, SSAS, SSRS
Oracle and Oracle BI

Data modeling tools
Netezza + Hadoop and Cognos

Greenplum
Mobile Apps

Teradata
Pentaho

Intetics Big Data Facts

  • Hundreds of years of experience in a variety of Data Technologies
  • Dozens of live solutions created including Data Warehouses, BI Reporting, Predictive Analytics, ETL and Platform Migration Project
  • Solutions developed for a variety of industries such as Food & Beverage, Retail, Automotive, Marketing, Media and More
  • 18 years of market experience
  • ISO-certified Quality and Data Security

Value-Driven Approach

1
Business Value Investigation

2
Data and Technology Consulting

3
Data Analytics

4
Delivery and Implementation

5
Support and Investigation

The growing world of Big Data

73% of executives have invested or plan to invest in Big Data projects in the next two years.

Data scientists are in huge demand, not least because of Big Data. Big Data refers to collecting, storing and analyzing large data sets to optimize business processes and gain insights into customer behavior.

Data analytics and business intelligence from big data is being used in a variety of industries, most notably in customer relationship management, retail, finance, marketing and security. Companies that do business over the internet are furthest along in Big Data.

According to a survey of 1600 executives, the most common of all big data projects were Customer Analytics (48%), Experience Analytics (45%), and Risk Analysis (37%). In 2014 a Gartner survey found that 73% of executives have invested or plan to invest in Big Data projects in the following 24 months.


The value of Big Data

As a result of Big Data Projects 80% of companies improved their decision-making process. The average return on investment was 46%.

Those who already deployed are reaping many organizational and market gains. Logistics and finance functions are expecting the greatest ROI (even though marketing and sales are currently the biggest investors in Big Data). According to a TCS study, the median per-company spending was $10 million (or about 0.14% of revenue) in 2012. As a result 80% have improved their decision-making process. The average return on investment was 46% (or about $900,000 return after subtracting costs), with Asia-Pacific and Latin American companies reporting greater returns than US and Europe.

Yet, most companies are still in the phase of strategy selection, as only 14% of big data projects were actually deployed in 2014 (up 6% from 2013). Looking forward into 2015 and beyond, this means that companies will start wrapping up their planning stage soon, and more big data projects will come to fruition.

That’s going to make the search for the perfect data scientist even tougher.


Who are data scientists?

By 2020, there will be more than a 100,000 data analyst shortage.

Big Data requires data scientists: analytical and communicative individuals

Data scientists can design and analyze data initiatives, communicate the results, and suggest what actions should be taken as a result. Data scientists are expected to have a variety of skills, ranging from technical analytics to business acumen and communication. They have been described as “part artist, part analyst”, “Renaissance individuals”, and even “unicorns”.

Unsurprisingly, there are few people out there that possess all the required skills, and even fewer education institution teaching these skills. While there are a number of programs that have been recently launched to remedy the lack of qualified individuals, the numbers of qualified data scientists is projected to be dangerously low compared to the demand.

For example, Gartner predicts that there will be more than a 100,000 analyst shortage through 2020, while McKinsey predicts a shortage between 140,000-190,000 analytics experts, and an additional shortage of 1.5 million managers capable of making decisions based on data.



Shortage or no shortage?

Many executives are skeptical whether one person can have all the skills required to be a data scientist.

Based on predictions, the industry has accepted (and is prepping for) a huge labor shortage. Yet, companies are increasingly launching big data projects, despite the predicted labor and skill shortage. While the rate of deployment remains low – only 14% in 2014 – it is growing (in 2013 only 8% of new big data projects were deployed). Moreover, the recent survey on executive big decisions suggests that the biggest reason for not launching new projects is not because of talent shortage, but because executives believe it is hard to generate data insights from their products and services. Yet, resource scarcity still has a high influence on big decisions.

This means that companies, facing an apparent lack of qualified workforce in the marketplace, are
(1) taking the time to develop their strategies before launching projects, and
(2) are trying to work around the problem of resource scarcity.

Instead of searching for the perfect unicorn, ehh data scientist, executives choose to either:


  • 1. Train workers internally

    Training workers internally seems to be a much more viable option to hiring someone new who doesn’t know anything about the company. It’s easier to teach someone how to use Hadoop, rather than how to run the business. It is precisely because of the profound knowledge of the business space required that executives choose to find and train workers from within the company.

    For example, GE is currently looking for 400+ data scientists to collect and analyze the data collected from its new sensor-equipped hardware. Thus far, they have recruited about half of that from within the company. They weren’t all data scientists, but GE created a special training program for their employees to teach them how to analyze the data. The result is a team who knows the company, knows its goals, and who is developing the skills to gain the necessary insight.


  • 2. Create big data teams

    Sometimes hiring internally isn’t an option. Still, many executives are skeptical whether one person can have all the skills required to be a data scientist. Moreover, finding and keeping that person (or two or three) and taking the full advantage of their talent might not be a possibility for the organization anyway. Not to mention, data scientists are expected to have a very deep understanding of the business as well as analytics. So if companies cannot find ONE person that has all the right characteristics, then why not create a team of people who would complement each other’s skills and achieve the same results as one data scientist would?

    Analytics teams used instead of scarce data scientists LinkedIn, for example, launched their first projects with a team of 2 engineers and 5 analysts, who were put at the very center of product development (and they went on to add new features to LinkedIn profiles such as Who’s Viewed My Profile, Career Explorer and other tools that now seem to be engrained in the interface). As the team expanded, it wasn’t all mathematicians and analysts. It included designers, web developers, product marketing, and operations.



Instead of searching for the scarce data scientists with experience and knowledge of your industry, building analytics teams, whether internally or with external help, is the best way to get going with your big data project.




Read Next:
What’s hot in technology: 9 terms to know (and use) in 2015
Creating better software products with crowdsourcing and outsourcing
Big Data Analytics: Why you need a business analytics expert and where can you get one

Images via Pixabay.

Big Data Analytics will get really big in 2014You’ve heard all the latest buzzwords: big data, data warehousing, business analytics…Why should you pay attention to big data in 2014? Here are some reasons to give big data more thought and why hiring a business analyst might help.

1. Everyone talked about it and now everyone’s doing it

In 2014 companies actually get a chance to put everything that was hyped about Big Data technology to practice. The technology is now advanced enough to get real insights from unstructured data (the reason behind the rise in use of big data in the first place). Many companies have begun using data warehousing solutions for storing enormous amounts of information, but also modifying it to create custom applications that better match their needs. The biggest challenge now is making sure that data can be analyzed in a productive way, and that it can be expressed in a way that is understandable and usable (because simply collecting and storing data is not making you any money).

2. Not just peer pressure – but possibility for deriving real value

The reason why companies are more likely to engage in business analytics is because it provides real value. Big data could lead to real productivity gains by many companies. For example Amazon derives 35% of its sales from product recommendations, which it makes using data from previous purchases (according to GP Bullhound’s report). Not to mention, the retail and manufacturing industries could experience an increase of $325 billion in annual GDP by 2020 from increased efficiency by using big data analytics as a productivity tool. So it is peer pressure – but with really good rewards.

Big Data Analytics: not just data warehousing, but effective analysis too3. Data availability will grow in the future

The availability of data is projected to increase over 44% in the next six years. That’s largely because customers will begin to give up certain aspects of their privacy for more personalized products. For example, receiving coupons for products that customers are likely to buy anyway creates a win-win situation for both retailer and customer. Either way, Big Data is here to stay and starting to experiment with it should be done sooner rather than later.



Think you might need a business analyst soon? Good.
Unfortunately, compared with the giant demand for big data analysis, finding the right business analyst can be a bit more complicated. The BA industry is projected to create roughly 2 million jobs in the coming years, but only about one third are expected to be filled. How can your company become a data-driven enterprise and make use of data-driven applications, especially if you’re not sure what your company data needs are or where to find the right people?

Big Data: It's not just the amount, but the variety1. Learn about the unknown

There are many people who still do not understand big data and what it can do for their business. Going to conferences such as the Innovation Enterprise’s Business Analytics Summit or reading articles about Big Data is a good start.

2. Determine your big data needs

Big data is not just about the amount of data you collect. Rather, “big-data is high-volume, high-velocity and high variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”. Does your business need to collect information on the weather to figure out if it affects sales? The more variety of data you want to process, the more complicated your analytics will become.

3. Get help

No one becomes a big data guru overnight. And if your company doesn’t have the capability to build a whole new analytics department from scratch, get help. If you don’t have expertise in data warehousing and acquiring business intelligence from your data, then it might be a good idea to turn to a company that has data experts you need.

Contact us for more information on how to get value from your big data.


Photo courtesy of gerard79 via stock.xchng

Intetics conducts continuos support of operational database (ODB) that manages the customer loyalty program of world’s largest furniture retailer. The ODB system also synchronizes data with the retailer’s central IT and Marketing databases on daily and weekly basis.

The system Intetics supports consists of several major components:

Furniture retailer’s custom coworker web application, designed for managing customer profile data. This application is used to issue new and replace lost loyalty cards and schedule Business Process Jobs.

Business Process Jobs that serve various marketing purposes. This includes several customer data reports, unsubscribe e-mail services using country-specific providers, registration statistics, and customer Card & Welcome Pack supply.

Data providers that are supplied by a marketing partner. This includes Kiosk solutions and web applications (depends on the country). ODB is developed and supported by Intetics team using proved & reliable open source technologies. Intetics now successfully provides services to the markets of 8 countries managing more than 5 million customer records. In short- and middle-term perspective the system will be expanded to more countries in which the retailer runs their business.