How Big Data can help your Firm Exceed your Peers

Big data got a lot of potential to advantage organizations and firms worldwide in any industry. It is just more than just a mere data and especially blending different data sets providing businesses with real data insights and knowledge that can be used extensively in the decision making and to improvise and enhance the financial position of a company. Big data could be explained more considering three V’s widely known as velocity, variety and volume.

1. Velocity – It refers to the speed at which data is developed, analyzed, stored and visualized. Earlier, when batch processing practice was commonly used, it was very normal to get an update to the database in a week or so. Servers as well as computers take their own time in updating the data in the respective databases. In such a big data era, data is built in real time situations and with the availability of all resources like internet connected devices, machines and others, it is damn easy to fetch and pass on the data at the right moment it has been created. The speed at which data is created and accessed is unimaginable. Just for a record, it has been estimated that about 100 hours of video get uploaded on You Tube, 30,000 images get uploaded on Flickr, 2.5 million queries get solved on Google, and over 200 million e-mails are sent every minute and more. The biggest challenge companies have been facing is the enormous speed at which data is created and used in real time.
2. Variety – Earlier, the data created was structured one neatly fitting in all rows and columns but those days are gone. But now scenario has been changed and 90% of the data generated by companies and firms is unstructured form of data. These days data is available in multiple forms and formats including structured data, semi structured data, complex structured data and unstructured data. The vast variety of data needs a different approach and technique to store and save all raw data. There are different types of data that demand different tools to use. The social media platforms give different insights and sentiment analysis on your brand whereas sensory data provides the formation about how the products should be used and what the mistakes are.
3. Volume – 90% of all the data was created in last 2 years and the volume gets doubled each year. The sheer volume is enormous and expanding a lot creating data with every second. In the past, data creation led to many problems and now days with lowering down of storage costs, and better storage options including Hadoop and related algorithms create meaning from all that information that is not a big problem at all.

It is for sure that data in itself is not only valuable at all. The value lies in the analysis done on that data asset and how it is transformed into information and eventually into knowledge and expertise. What matter is how organizations will use and value that data and turn it into information centric company that is based on their decision making on insights fetched from data analyses? By analyzing and keeping a keen eye on all the data in your firm may find arenas that can be improvised and can be conducted in a better way. Especially in the logistics industry, it is extremely vital to make data more efficient and sources available in the supply chain. There are generic use cases that blend a small portion of the immense possibility of big data recommendation engine showing endless opportunities to take benefit of the big data. Organizations and firms that are key players in this industry including IB Technologyprovides a different big data approach and making apt usage of such possibilities enhance business value and assist to stand apart from your competitors.

Managing Disorganized Data across the Enterprise

Organizations and enterprises should start across the world for the development of a bottom-line for future state functionality required to efficiently manage and cultivate the growth of unstructured data. The conclusion was endured and toted out at the peer incite research meeting where major well known four practitioners working in the key renowned regulated industries including healthcare, finance and energy discussed and shared their viewpoints on the primary barriers of deploying successful and promising IM initiatives. The final decision demands action and response from both users and vendors end to build, specify, test and deliver a future state information related functionality and management architecture. The right tactic is to analyze and think about the automation and functionality requirements across diverse technology platforms including messaging, storage, file servers, desktop, policy engines, content repositories, user directories, cloud and portals in the substance of a strategic information management framework. There are approximate nine major categories of functionality that must be considered and thought of at a minimum level.

1.       Policy and rules management – No matter whether imposed by internal policy, jurisdictional law and regulation, processes and business agreements, the functions are defined for creating, maintaining and enforcing information management policies. Acted as the central post of the information management, rules management and policy must operate across technological platforms. The best examples of them involve disposition, policies for retention, use, information assurance and distribution.

2.       Content management – The functions are available for enabling policies for building, templating, storing, capturing, retaining, version managing and holding, collaborating and preserving information.

3.       Configuration management – Along with building custodial and ownership responsibilities and business application dependencies, the functions are configured for maintaining the overall prototype and sketch of the information management environment.

4.       Classification and declaration – The functionalities for enabling policies needed for the classification and the declaration of the data involving the capability to distinguish in between business records and non-business data and categorizing it according to the policy attributes.

5.       Collection and crawling – The functions needed and apt for gathering and locating unstructured form of data dispersed across the information management environment.

6.       Information access and discovery – The defined and apt functions needed for unified indexing, searching and discovery purposes.

7.       Copy management and creation – The functions that enable rules authorizing and governing the development of data, copies required to be maintained and the redundant or single instancing of information.

8.       Information assurance – The functions that enable policies for information authentication, identity management, privacy control, access management, auditing and use management. Just for an example, the functionality let the users to determine the authenticity and reliability of business records along with establishing and maintaining policy based relationship in between the data and users.

9.       Analytics and reporting – The functions that enables for alerting, monitoring and real time reporting on key information management events including configuration changes, policy updates, classification events, security anomalies and hold requests.

In addition, sometimes you don’t wish to outgrow your MDM Pharma system. You simply do not require requesting for extra funding, grabbing knowledge about another new system or agitating the entire IT environment simply to master a new subject arena or domain. The multi domain MDM solutions avoid the same simply by enabling you to access master data and all other key subject areas. In addition, you can actually use such prestigious relationships in between the data domains to improvise your business. For example, think of your MDM system is handling both customer as well as product information. With multi domain MDM solutions, it would be easy for you to see the products along with up selling and cross selling those to respective customers. In all, the leading service providers of MDM solutions are IB Technology , Informatica, Sigma Systems, Abiogen and Trinity Software Solutions.

The Business Analytics Loop and Big Data Applications

Business analytics is all about transforming irrelevant and meaningless data into valuable insights. For analytical practices and approaches to reach their full and optimum potential, business analytics tools and platforms are required to make it easy for customers to take action based on their new insights. They must analyze, feed and monitor the final results of actions taken back into the system in an automated and systematic way to improve and inform the predictive models that led to the insights at the very first place. One can actually consider this process as of closing the big data analytical loop. For an example – A business consumer at a big vendor uses self business intelligence tools to decide whether to offer or not a new promotional campaign. In between user analyzes that the three hour online sale will likely result in significant revenue generation based on the intelligence yielded by the underlying analytics engine. The user has to implement the campaign, keep a track of its performance and makes necessary adjustments to future ones as assured and guaranteed.

In existing environments, the foremost step needs the user to exit the business intelligence application and log into a different campaign management system to raise the sales and its related promotional efforts. The user afterwards waits for a day or two to get the canned and static report with metrics and finally suggest to a central analytics team to adjust the analytical models based on the positive or negative results. If the user is lucky, those suggestions might make their way into the analytic process and loop will never be closed. Customers continue to make decisions entirely based on the analytics and intelligence essentially frozen and numb in time till someone lifts the chain to redo the entire process. Again the whole process gets repeated.

Closing the business analytical loop needs essential anticipation into how data and application infrastructures are crafted and defined. Big data visualization strategies, analytical engines and transactional systems should be capable to bi directionally interact in real time situations. The transactional and BI applications should be tightly blended with the functionality built into BI applications. In an ideal manner, non relational databases should play a role to board multi structured data including social and machine generated data. Cultural issues should be overcome and no longer be an excuse acceptable from IT pros, analysts and business user’s side for clinging on inefficient, outdated and hoaxed analytic processes. So the next time the user conduct a similar kind of analysis, the big data recommendation engine suggestions generated will be informed by the relative success or failure of real world campaigns and the entire process will get repeated. Moreover more iteration in the recommendations and analytics improvises and increases the revenue generation.

Along with this, big data pioneers and supporters maintain that the sheer volume of data and information diminishes the effects of occasional poor quality of data. If you are browsing for peta bytes of data to determine historical trends, a few data input issues will barely list as a glitch on a dashboard or reports. It does not mean that the data quality is not vital to big data. It is true in real time transactional situations. Likewise big data applications that recommend doses and medicines for critically ill patients are better be entrusting over good data. Same goes for big data operational applications that affirm commercial aviation, industrial internet use cases and the power grid. It is mandatory for professionals to acknowledge the source and structure of the data along with the data quality prerequisites for apt big data use cases including recommendation engine in order to evaluate the type and quality of tools and measures to apply. And, the biggest key players dealing in big data and its real time use cases are IBM, Oracle, Microsoft, CSC, 

IB Technology , Informatica and Infosys.

Real World Big Data Use Cases

What actually makes big data technologies including Hadoop and others so compelling and imperative are that they let enterprises and firms to figure out the answers of the questions they didn’t even know to ask for. It can result in deeper insights that drive to new product ideas and assist in identifying ways to improvise operational efficiencies. There is a wide range available in the number of already identified big data real world use cases for leading web giants like Google, LinkedIn, Facebook etc. Some of them are mentioned below in brief.

1.       Recommendation Engine – Online retailers and other web properties make use of Hadoop to suggest customer’s products and services based on the analysis of users profile and their behavioral data. There has been a vast usage of big data recommendation engine used by umpteen applications like LinkedIn empower its visitors by offering them “People you may know” feature and Amazon by suggesting related products for buy to online users.

2.       Sentiment Analysis – Mainly used in combination with Hadoop, and advanced text analytics tools determine the unstructured text of social networking and social media posts including tweets to evaluate the user sentiment detailed to specific companies, products and brands. The analysis starts from macro level sentiment down to the individual’s consumer sentiment.

3.       Risk Modeling – Banks, financial companies and others make use of next generation data warehouses and Hadoop to determine big volumes of transactional data to conclude risk and wide exposure of financial assets, prepare for targeted “what-if” scenarios completely based on counterfeit market behavior and score potential customers for risk.

4.       Fraud Detection – It is recommended to make use of real time use cases of big data techniques to blend historical and transactional data, customer behavior to detect any fraudulent activity. For example – credit card companies use big data technologies in order to identify transactional behavior that represents high likelihood especially in case of stolen cards.

5.       Marketing Campaign Analysis – Big data technologies allow marketing teams of companies to incorporate larger volumes of increasingly crude data such as call detail records and click stream data to raise the efficacy and preciseness of analysis.

6.       Customer churn analysis – Companies use big data technologies and Hadoop to determine the user behavior data to analyze the patterns that symbolize which customers are more likely to leave for a competent service or vendor. Actions would be taken further to prevent to retain the most profitable customers.

7.       Social Graph Analysis – In alliance with next generation data warehousing, social networking and Hadoop, data is mined to evaluate the customers that have the most likely influence over others inside social networks. The process helps firms to determine who the most important customers are, and those not purchase the products and spend much and more related to the same.

8.       Network Monitoring – Big data technologies are employed to analyze and showcase data collected from storage devices, servers and other IT hardware to let administrators to counsel network activity and identify bottlenecks.

9.       User Experience Analytics – Many enterprises use big data techniques to incorporate data from different user interaction channels like online chat, twitter, call centers etc. to gain a complete view of the user experience enabling companies to acknowledge the impact of one channel on another and optimize the entire user lifecycle experience.

10.   Research and Development – Companies including pharmaceutical sector make use of Hadoop and big data to go through huge volumes of text based research and historical data to help in the development of new products.

Above mentioned use cases are the samples and most compelling ones of big data. Though, many more have yet to be discovered and this is the promise of big data. Adding to it, the key players offering big data solutions are IB technology , Amazon, Datameer, Attivio, Zaponet and Microsoft and Caserta Concepts.

Big Data and Business Analytics

The introduction of web, newer technologies and mobile devices has caused a fundamental change to the nature of the data. Big data has its own distinctive and unique qualities that has differentiated and distinguished it from conventional corporate data. It is the information that is no longer centralized, highly structured and well manageable now as compared to the traditional form of loosely structured and distributed data. In general, it contains the following mentioned attributes.

·         Volume – The volume of data developed both inside and outside the firewall and corporations through web, infrastructure, mobile devices and other sources is rising exponentially every year.

·         Speed – The speed at which this new data is developed and the requirement for real time analytics to fetch business from it is rising to the digitization of mobile computing, transactions and the number of mobile device users and internet.

·         Type – The types of data are increasing at a speedy pace mainly in form of semi structured and unstructured text based form including log file data, location based data and social media data.

On a wider note, big data could be generated from a large number of resources like mentioned in brief in below.

·         Social Media – At present, there are about 150 million public blogs, 260 million twitter users and 700 million Facebook customers. Every tweet, blog post, comment and Facebook update creates multiple new data points available in the form of structured, semi structured and unstructured also known widely as data exhaust.

·         Mobile Devices – There are approximate 5 billion mobile phones that are in use across the world. Each text, call and an instant message is treated as data. Mobile devices especially tablets and smart phones make it simpler to use data generating and other social media applications. Mobile devices also gather and transmit location data.

·         Internet Transactions – Billions of stock trades, transactions and online purchases used to happen on daily intervals comprising innumerous automated transactions. Each develops a wide number of data points collected by banks, retailers, credit agencies and credit card service providers.

·         Networked Devices and Sensors – Electronic devices of all kinds comprising IT hardware, servers, smart energy meters and temperature sensors develop semi structured updated log data that records every action.

Traditional data management and other data warehousing tools are not up to the mark of analyzing and processing big data in a cost effective and timely manner. To be precise, data must be handled and managed effectively into relational tables having neat rows and columns prior to conventional enterprise data warehouse can absorb it. Due to the requirement of manpower and time, incorporating such structure to bigger chunks of unstructured data is quite illogical. In addition, scaling up a traditional enterprise data warehouse to accommodate potentially Megabytes of data would need unrealistic and authoritative financial investments in new as well as oftentimes proprietary hardware requirements too. There could be many different ways used and applied to analyze and process big data. They take benefit in general of commodity hardware to enable scale out parallel processing strategies, bestow non relational data storage capabilities in order to process semi structured and unstructured data and apply modern data analytics and visualization technology to convey deep insights to end users worldwide. Last but not least, IT firms and organizations like IBTechnology assist others in determining the most reliable and practical big data use cases and create products and services by making use of latest technologies and big data recommendation engine easier to implement, use and manage along with helping out the customers in giving the flexibility required to experiment with new big data tools and technologies.