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July 8, 2018

Navigating your next Customer experience

Posted by Arav Narasimhamurthy (View Profile | View All Posts) at 1:08 AM

Customer experience is on every CEOs agenda over decades however explosion of digital and abundance of choices have empowered customers more than ever before to make decisions without blink of second thought, this has made organizations to rethink, make it a top priority item and CEOs get more involved in the customer strategy than ever before.

Gartner quotes by 2020, the customer will manage 85% of its relationship with an enterprise without interacting with human and customer experience is the new battle field.

Jeff Bezos, CEO of Amazon says "We see our customers as invited guests to a party, and we are the hosts". Amazon provides one stop experience to customers across channels, it is expanding its foot print across industries. Amazon recent announcement of recruiting entrepreneurs to run local delivery networks and acquisition of online pharmacy Pill Pack has wiped out $17.5 billion from eight companies market values in one day, such is the impact of organizations which are leveraging data and analytics to provide personalized, differentiated and relevant customer experience.

Companies have started to realize that the most sustainable form of differentiation is customer experience and not product innovation or low cost solutions. Technology aided with human emotions is key in creating these differentiated customer experience for any organization.

Few key challenges to be considered while strategizing and navigating your next customer experience solution

1.       Gaps and shortened customer journeys - Current generation customers hops at least 3 channels before completing transaction. Days are gone where customer journey is treated end to end from acquisition to servicing. Companies need to incrementally improve experience at every step, boldest is to redefine and expectation and enhance experience in the process.

2.       On-demand era - Every industry has been touched by on-demand expectations. Customers need personalized service and experience on demand, this need gave birth of Amazon dash button. Didi Chuxing, car sharing platform has more than 21m drivers serving 25m rides per day which is more (appx twice more) than Uber.

3.       Automation and Self-improvement service - Once the minimal needs are met, customers look for automating mundane touchpoints with companies and expect the organizations to automate these (reminders, recommendations, recurring events, pattern recognitions etc). With the connected world, it is possible to build positive experiences with minimal interactions. Didi Chuxing has built AI engine allows customers to just say "I need to go, right now" using mobile phones, AI engine knows the type of car person needs, where he/she has to travel and sometimes it proactively offers a ride depending on the pattern, schedule and information.

4.       Hybrid or channel agnostic - It is important to provide consistent customer experience at every touch point of customer journey as clients likes to hop from one channel/device to other channel/device. Ex: Netflix, HBO, Amazon prime provides seamless transition which you switch watching from one device to other device.

5.       Customer data and privacy - Customer would not compromise on data privacy and sharing to customer experience. Both are equally important to them, it is critical to identify the information and mechanism by which it can be shared externally to provide seamless experience

Infosys leverages Customer genome solution to understands their DNA and fill in the gaps to navigate the next customer experiences

https://www.infosys.com/navigate-your-next/Documents/customer-experiences-digital.pdf?soc=tw167045

Most progressive organizations are leveraging technology aided with human to address these challenges. Augmenting existing investments in legacy systems and talent with new and emerging technology would accelerate creating differentiated customer experiences.

https://www.infosys.com/digital/insights/Documents/accelerating-digitization-nextgen-integration.pdf

Emerging technology capabilities aiding to create differentiated customer experiences are

1.       Artificial Intelligence and Machine Learning

       AI, once a mostly academic area, today integrates and enables a constellation of mainstream technologies that can have substantial impact of customer experience.

       AI and ML powers technologies that overlay humans to provide an oversight and tracking mechanism to employee actions, helping with compliance, security, and the monitoring of actions.

                Foundational blocks of any AI/ML driven organization are

·         Business sponsorship - It is inevitable to have alignment and sponsorship from business to integrate insights generated by AI/ML applications into operational systems/process to generate value added services and create relevant experiences for customers.

·         Big Data ecosystem - Organizations need to invest in standing up Hadoop infrastructure and build data lake (Raw, curated layers), semantic layers to access legacy environments, API driven micro services and ability to experiment and productionize insight generation applications.

·         Digital Integration - Adopt design thinking approach to integrate insights, AI apps, virtual assistances across SMAC

·         Data science - Data science enables organizations to create relevant customer experiences throughout the journey by using NLP, supervised and unsupervised ML models. Ability to learn, reinforce and adopt on real time basis by active learning makes it different from traditional analytics.

Infosys has been partnering with various client to bring AI to life at enterprise level. You can find detailed success stories by visiting below link

https://www.infosys.com/navigate-your-next/ai-powered-core/Pages/index.aspx

2.       Conversational assistants and digital advisors

 Customers today expect 24X7 customer support. Conversational assistants and advisors aided with NLP and AI/ML creates digital opportunities to build and support customer needs.

 "Gartner predicts for next decade; it will be Conversational AI first over cloud or mobile for most of the organizations."

Foundational components needed for building conversational applications are

·         Enterprise driven - Identify and build/buy platform which would meet enterprise needs across support centers, mobile applications, online assets and internal service desks

·         Data security - These applications generate humongous volume of data, organizations need to strategize where and how to store so these can be used for enhancing customer experiences and monetize data

·         Time to Market - It is critical to align on the goals so you can decide on the capabilities, platform, time to experiment, competitive analysis...etc

·         Platform - Identify the target channels you to serve so you can finalize the technology stack and infrastructure for assistants and advisors

https://www.infosys.com/services/microsoft-dynamics/Documents/intelligent-bot-services.pdf

3.       Immersive media through Artificial reality, Virtual reality and mixed reality.

 For long time AR/VR was seen in the context of entertainment industry but last few years it has significant impact in our daily lives.

 Experiences are becoming real by leveraging immersive technologies. Challenge with traditional mobile and online is you cannot experience or feel it. Example, customers do not buy perfume on phone as you cannot smell it. If you are able to stimulate the experiences, then they are all in for it. Certain brands have created artificial worlds providing real experiences to shop and try items on them before purchasing.

 Augmented reality amplifies human experiences of what they actually will get, KabaQ an AR driven app provides customer real time experience to see virtual 3D food on their table in-restaurant when ordering online.

 AR/VR are pushing the imagination of humans beyond boundaries to build applications across Retail, Manufacturing, Health and Insurance, Financial services industry.

Infosys is experimenting and building applications leveraging AR/VR to provide differentiated customer experience

https://www.infosys.com/ar-vr/Pages/about.aspx

Organizations are looking beyond these to create differentiated experiences and create new digital business opportunities.

April 27, 2018

Given May 25 is round the corner, what do organizations need to do in view of GDPR?

Posted by Rohan Kanungo (View Profile | View All Posts) at 7:36 AM

The EU General Data Protection Regulation (GDPR) comes into force in exactly 1 month, on 25th May 2018. As deadline is approaching, GDPR demands that organizations should be able to demonstrate compliance with its data processing principles.

For many organizations, it is not possible to achieve GDPR compliance by 25th May, 2018, if they have just started their GDPR implementation. In such situation, companies should concentrate on how to prioritize those areas of GDPR where failure to act would leave organizations with potential penalties. Companies must be able to show the proof that they are taking appropriate measures to comply with the GDPR regulation.


Let's look at 5 key areas which organizations should focus on in order to bring their company on right GDPR path in a quick way.


1.     Be ready with GDPR implementation plan

Organizations should make sure that overall strategy for GDPR compliance is in place. It is important to demonstrate a road map & commitment to address GDPR requirements complimented by the tools, technologies and resources. GDPR implementation plan should be able to give clear picture of:

  • Where personal and sensitive data is stored?
  • How the data flows within and outside the organization?
  • Personal data collection, generation and processing practices
  • Roles and responsibilities; governance and accountability
  • Required changes in internal/external processes and privacy documents
  • Training and Education Program

 2.     Make sure data breach response procedure is in place

As per GDPR, data breaches must be reported to customers and the data protection authorities within 72 hours following the discovery of the breach. That's why it is important for the organizations to ensure that they have an efficient system in place to detect and react to any breaches in a timely and effective manner. As GDPR enforcement is right around the corner, companies should at least ensure that policies and procedures are in place to identify, inform and inspect breach within the timeline.


3.     Designate a DPO (Data Protection Officer)

If organization is a public body, systematically monitors data subjects on a large scale or handles special categories of protected data then they must employ a Data Protection Officer (DPO). DPO acts as a point of contact and should be fully resourced and supported to lead company's GDPR compliance program. So, it's a good way to show that organization is on right track of GDPR compliance journey.

Even if organizations do not officially need to appoint a DPO under the terms of the regulation, they should ensure sufficient staff with designated responsibility to deal with compliance.


 4.     Be ready to deal with data subject's personal data requests

According to the GDPR, individuals have the right to access their personal data, the right to correct inaccurate personal data, the right to have personal data erased, the right to restrict the processing of their information and the right to move personal data from one service provider to another. Organizations must be able to demonstrate that they can respond to a data subject's personal data requests within the time frame. Organizations should make sure that plan is in place to validate and identify requesting data subject, provide platform for data subjects to create all type of requests and respond to their requests within time frame. Organizations should update their privacy policy and notices and let the customers know how they are planning to handle their requests.


 5.     Conduct GDPR training programs for employees

It requires lot of effort by every organization to build data protection into its culture and into all aspects of its operations. Employees need to be actively engaged in and supportive of the GDPR compliance project. Creating GDPR awareness by conducting training and education programs plays a vital role here.

April 16, 2018

VISITOR/PROFILE STITCHING IN THE AGE OF GDPR

Posted by Rohan Kanungo (View Profile | View All Posts) at 10:02 AM

(1) Use of cookies or similar technologies: Whenever you set cookies or similar technologies on a user´s equipment for marketing purposes, you need to obtain cookie consent. Cookie consent would need to be provided by all affected consumers. This is not safeguarded if different consumers use the same device once one consumer has provided consent and the cookie settings store this choice. However, this problem is difficult to overcome in practice.


Regarding the tracking/profiling also on third-party websites, the use of a cookie to track consumer´s behavior on third party websites before it enters your website cannot be legitimized with cookie consent only.


2) Collection and processing of consumer´s personal data: The most sensitive issue is the justification for the collection and processing of consumer´s personal data (such as consumer´s browsing habits in connection with its ID etc.).


Tracking/profiling through account: If you track consumers through their account we think that the profiling may be justified without explicit consent but based on customer's legitimate interests. You may argue that account holders are existing customer (where GDPR generally allows broader leeway. Aspects which need to be considered with the balancing of interests in our view:


  • Privacy intrusion is little when ads are merely shown on your website;
  • Personalization only relies on information gathered from your website (and not from third-party websites);
  • Consumer is an existing consumer and is informed about that tracking via the Privacy Policy; and
  • Consumer can also withdraw its cookie consent at any time to end the tracking (as it is usually emphasized in the Privacy/Cookie Policy)

Tracking/profiling through device:


  • Tracking/profiling restricted to your website: If you track consumers through their device on your website only, we think the collection/processing of personal data in relation to existing consumers (i.e. those with account) can still be based on legitimate interest. In relation to consumers without account, we do not think that the justification of legitimate interest will work. This issue is a dark grey area, requires a risk assessment and discussion with your DP team.
  • Tracking/profiling also on third-party websites: We do not think that the collection/processing of personal data on third party websites for marketing purposes can be based on legitimate interest alone. This tracking is very sensitive and would hardly be acknowledged as covered by legitimate interests that outweighs the privacy interests of the consumer by data protection authorities ("DPAs"). We recommend that at least the most sensitive part which is the collection /processing of personal data should be covered by a proper GDPR consent.



BE GDPR - READY WITH INFOSYS

https://www.infosys.com/gdpr/

April 12, 2018

Who should drive the GDPR Program?

Posted by Rohan Kanungo (View Profile | View All Posts) at 7:15 AM

There is an increasing awareness of GDPR regulations and organizations are coming to terms with it. Having said that, many are grappling on how to structure and execute the program. Why is this a vexing problem? Structuring the GDPR program is not a trivial task. While past experiences in delivering security programs and regulations can provide some guidance, it cannot be replicated in the GDPR scenario. The primary reason for this is because of the nature of the GDPR itself. GDPR is not a 'prescriptive' document, it does not lend itself to a 'check list' that can be deployed. May be couple of years down the line, it could be possible, but not right now. GDPR requires subjectivity and interpretation; 'Risk Management' and proportionate response in accordance with the risk threshold is inbuilt into the structure. Coupled with this is the fact that while the 'intent' of the regulation is clear, there are several grey areas when it comes to contextualizing and operationalizing it to a specific business case. Secondly, data security and protection is in a 'Darwinian' moment. Stakes with GDPR are high. It is being looked upon as a 'role model' in terms of data privacy regulations and in many ways will pave the path for future action in this space. Organizations are acutely aware of this and they are determined to make an informed and calibrated decision on how to approach this situation. The costs associated with a tepid initiation of GDPR will be manifold and will set the organizations' back significantly.

Key Success Factors

What is required to deliver any GDPR program is a high level of management awareness, the right organization, efficient tools, employee education, and an effective implementation model. 

The key success factors for a delivering a GDPR program are -

1.    Alignment to overall Business Strategy & Operations

2.    Decision Making Mandate

3.    Budgetary Control

4.    Ability to drive organization & create awareness 

5.    Ability to execute

We are of the opinion that only a combined implementation model is effective in achieving and demonstrating compliance. Combined efforts are typically required to achieve a clear mapping of regulatory requirements to the entire organization and all its operations, including IT.

We recommend a 'GDPR Task Force' to be constitute under the auspices of the Office of the CEO. This task force will be led by by the CEO and will have representation from all the departments of the organization including the CXO suite and all the business functions - CFO, CIO, CDO, CSO, Legal, Marketing, Sales, HR, Procurement etc.With its wider management focus and with project groups across different functions--such as legal, marketing, and IT--will help with strategic considerations, since it reviews what customer data is collected, how it is used, and how it could be done better to create competitive advantage. This ensures that "privacy by design," as required by the GDPR rules. Privacy by design means taking data protection into account at every step of a company's processes, from R&D and business development to marketing and sales.

BE GDPR - READY WITH INFOSYS

https://www.infosys.com/gdpr/

April 10, 2018

GDPR -Managing Data in the Digital Age

Posted by Rohan Kanungo (View Profile | View All Posts) at 2:55 PM

Hallways of businesses across the world, especially in Europe, are abuzz with the newly minted regulation -- General Data Protection Regulation (GDPR). As an upgrade to the previous Directive 95/46/EC, the GDPR upholds the rights of EU citizens to protect their personal data irrespective of the location of processing. The recent fracas with Facebook and unauthorized usage of personal data has brought data security and privacy into the public domain in a never before way. Today, most individuals are eager to know how their data is being used and what are organizations doing to ensure that their interest are adequately safeguarded.

 

The central theme in GDPR is data privacy as a fundamental human right. GDPR is unique because of this fundamental assertion that data is now central to our way of life, and therefore, its treatment cannot be trivial or an afterthought. But then, the prevalent model of data usage and treatment is not holistic and it is not focused on the right way of handling this asset, but on a narrow vision of collecting data and then curating it without an overall harmonious strategy. The basic question of the times that we live in then comes down to addressing this question of how do we handle this -- do we continue forward on the path of collecting and using data by whatever means possible? Definitely Not.

 

In this digitally enabled world, data is all-pervasive. It is driving the business. Unimaginable quantities and varieties of data are moving to and fro in the digital world. In this highly fungible ecosystem, it is a matter of fact that personal data and sensitive information is collected, maybe curated, and then made available for consumption. There are very few organizations who can confidently state that they have a complete handle on all the data elements in their organization.

 

Hence, we believe that adopting the GDPR process will make companies review their data management policies and processes, and evaluate if their data organization is aligned to the digital world and the new-age economy.

 

GDPR is not a set of isolated activities pertaining to legal, consulting or data management, but a combination of different processes working integrally

 

Adopting and assimilating GDPR in the ethos of your organization will be a catalyst for taking the necessary steps to build strong digital capabilities and creating a competitive advantage. Some of the key initiatives could be -

 

Data Discovery & Classification - identifying all personal data lying in fragmented or scattered systems; then categorizing to help understand the type of data within the organization and associated risk of exposure.

 

Data Cataloguing enabling organizations to understand and form data relationships to various business processes regardless of its sources and platforms.

 

Data Standardization - cleansing and consistent formatting of data coming from disparate sources subjecting it to further transformation.

 

Data Profiling & Quality Checks ensuring data accuracy and its completeness in a holistic fashion

 

Data Ownership defining clear specification of data controller's rights while modifying and deleting personal information of an individual. It also advocates for recording consent of data subjects' for storing and processing their personal data.

 

 

GDPR reinforces what has been a best-kept secret in the industry that data holds the key to competitive advantage, and treating data strategically will be a key differentiator between being hugely successful and just scratching the surface.


BE GDPR - READY WITH INFOSYS

For more information on Infosys GDPR, visit https://www.infosys.com/gdpr/  



March 28, 2018

AI Reborn

Posted by Shahnawaz Qureshi (View Profile | View All Posts) at 11:52 PM

 

During my academic days we had a dedicated subject for Artificial Intelligence (Al) in our Final year. Of what I could remember, it was all about Algorithms and a unique less known language called LISP (list Processing) for which we had labs back then. It was one of my favorite subject as it was futuristic and not normal science. But coming out of college I believed that Al was something that would continue to be a research topic rather being a practical implementation. Why? Because how would you come up with an algorithm that could mimic Intelligence? It is one thing to write a code for Chess that could analyze all the "n" possible moves and choose the one that has the smallest high probable path for victory but it's totally a different case to provide a generic Intelligence. Those were the days post IBM Deep Blue's victory stories against Human Grand Masters. So, called intelligence in domain like chess is quite possible as it has boundaries. There are limited rules and probable moves. A good set of algorithm and powerful processing power would give any grandmaster a run for his money. But in a more variable domain, things get more fiction than being realistic.

Fast forward seventeen years and Al seems to be more realistic and evolving towards realization. And I get a feeling YES, it's possible. So, what has changed? To answer this in two words -"Data Analytics".  Today's AI is driven by Data Analytics, Algorithms being developed are focused more on Data. Other than this most of the advancement in Today's Information Technology landscape is aiding Data Analytics.

The foundation of Today's AI has been data; algorithms under the field of Machine Learning and Data Mining are focused around data and harvesting patterns from it, these harvested patterns forms the building blocks of today's AI. Unlike yesteryears, today data is available in huge volume, the digital world around us has been spewing data all around, and this data in form of Big Data provide the mining field for patterns which usually remain unseen on surface until right analytics has been applied. With real time analytics, intelligence no longer need to be harvested from historical data but can be attained and recognized as it happens.

Transformation in technology landscape has aided the evolution of AI as well. Cloud computing is providing virtually unlimited storage and computing power required to run data analytics at scale. Another crucial element that is contributing to the success of AI today is "Connected System" through Web services and API's. AI yesteryears was conceived to be more of a monolithic, an AI system back then was supposed to house most of its intelligence capability, but today intelligence is distributed. Today if you want your system to recognize human speech than you need not build it from scratch but can leverage existing speech recognition services from Microsoft, Google or Amazon. Similarly if you need an image recognition capability than you can look forward for Vision API's again from players like Microsoft, Google and Amazon.

Just like distributed architecture the evolvement of AI has also been distributed and community driven. Popular statistical computing language "R" widely used for Data Analytics is open source. What makes "R" so popular for data analysis is its vast range of packages developed and contributed by independent developers solving certain problem domains and making it available for reuse this collective effort has been contributing towards the advancement of Data Analysis and AI.   

These are some of the factors providing the right ecosystems that is making AI to thrive and become a reality today. As hinted above no one entity is building the whole AI ecosystem but it's been evolving gradually in bits and pieces. Bunch of these services are baked locally to provide certain AI implementation like "Self Driving Car". Gradually such focused implementation can be augmented with one another to give rise to more intelligent system with broader capability that could someday rival humans. And probably one day, tables might turn around and machines might be working on making humans more intelligent and efficient so that we humans can serve their purpose, Scary uh!

October 31, 2017

Deciphering the Minority Report on AI

Posted by Ramaswami Mohandoss (View Profile | View All Posts) at 5:29 PM

If Thomas Friedman decides to revise his best seller (The World is Flat) in the future, I think he would include AI/ML as an important disruptor. Per Jeff Bezos, Artificial Intelligence is in its golden age. Jeff calls AI an enabling layer that will improve every business. At the World Economic Forum in Davos, Satya Nadella said AI could be a vital driver for growth. Mark Zuckerberg predicts AI to deliver many improvements in our lives in the next 10 years. Google co-founder Sergey Brin says he's 'surprised' by pace of AI and calls it revolutionary.

But not everyone seems to be on the same page. Not Elon Musk for sure. Last year, he compared AI to summoning the demon. This year he went ahead and called it the biggest risk we face as a civilization. While the majority seem to feel positive about AI, it's hard to ignore the minority report, especially when it comes from one of the most respected visionaries in the current times.

So, what is the truth? Is AI really a threat to our existence? Categorizing machines into 4 kinds and evaluating the risks introduced by each of these, I have tried to find an answer to the earlier question.

  1. The flawless clerk
  2. The expert system
  3. The invisible machine
  4. The silicon poet

For the complete perspective, please click here

June 2, 2017

Artificial Intelligence - Unconventional use cases which will be reality soon.

Posted by Arav Narasimhamurthy (View Profile | View All Posts) at 9:46 PM

As our CEO, Dr. Sikka says AI - Pursuit of building something intelligent is as old as humanity. It has grown leaps and bounds from the time AI was discussed in 1956.

Since its inception for what determined a machine to be "intelligent" AI has evolved overcoming challenges during 90's and has entered its golden era.

Increasing investments from Nvidia on GPU and Google in TPU has made Moore's law more and more relevant in current scenario. Availability of these powerful processing units have accelerated Deep learning, Automate Machine learning and artificial neural networks.

Startups have revolutionized AI world and are providing disruptive solutions to solve complex business problems, these days every organization is adapting AI in some or other forms, may be as simple as for customer segmentation, social integration, personalized offers, supply change or building complex solutions for solving human problems around cancer treatment, self-driving cars and many more.

Over next couple of years maturity of AI will rapidly increase and more unconventional use cases will turn into reality for consumption.

Here is first in series of few such use cases in my view as Infrastructure, platform and products becomes available to implement.

Emotional AI:

Deep learning is the study of artificial neural networks related to machine learning algorithm containing more than one hidden layer similar to human brain. Based on deep learning AI can distinguish between dogs and cats, good vs criminals but how do you feel when you are treated from a robot, how does families feel when AI is able to identify depression in struggling students?

Among lot of other things AI is able to recognize faces, turn sketches to pictures, identify voice and many more.  

Days are not far when Siri or Alexa can detect emotions based on the sound of your voice and have a conversation, recommend a therapy session or send an alert to your loved ones to order a bunch of flowers to make you happy.

As human beings, we understand contexts and empathy. Not many AI models have it today. Companies that can implement these into their technology will have more success.

AR Chatbot:

2017-05-29-PHOTO-00000140.jpg

Chatbot is common platform these days and everybody has a version of right from Microsoft, Facebook, Watson, Google or custom built.

People are probably going to be more drawn into engaging with chatbots which has personality; it has to be companion to whom people can engage with.

AI integrated with augmented reality can do wonders. If bots can be integrated with AI, emotional AI and AR then humans-robot interactions will take a huge leap forward. Humans often struggle with appropriate responses due to complexity of emotions, if technologies can decipher this then the output will be very impressive.

To be continued...

May 10, 2017

How Advanced BI Tools can add value to the Shipping and Liner Industry

Posted by Mangalika Ghosh (View Profile | View All Posts) at 6:42 AM

 

With the world wide growing demand of faster business decisions, the Liner and Logistics companies should definitely start trying advanced reporting tools like Tableau or Qlikview etc as a powerful weapon in container forecasting, demand driven supply chain and other trend analysis.

Some shipping giants are still using traditional BI or manual query executions or sometimes manual graph preparation in excel to understand trend and improve business KPIs. These methods are not only time consuming but also effort intensive. Who does not agree that viewing a whole lot of data in terms of instant pictorial presentations is way better than browsing monotonous excels and comparing the numbers or sums.

Take below examples, where you can quickly get a comparative trend of payment against shipment delay per Shippers.

[Samples from Tableau site]

Hovering mouse on a specific dot gives you other details like Shipper/Shipment numbers/Delay in hrs/route etc in a pop-up window.

Clicking on a specific dot, the dashboard will show further details including average delay etc by that shipper.



Generic Shipping LOBs where advanced reporting tools can definitely benefit the users:

  1. Better Asset utilization and optimization by single dashboard with pixel perfect visualization

  1. Container utilization by regions/by months
  2. Vessel allocation efficiency by regions/ by terminals

     

  1. Better Cost Management by sharable and easily customizable Dashboard:

  1. Revenue per TEU by routes/ ports etc.
  2. Vessel Operation Budget vs Actuals by locations/by months etc.
  3. Balance Outstanding per shippers

     

  1. Better Productivity by single dashboard with heat-maps, scatter plots etc:

  1. Productivity by booking offices etc.
  2. Work Order target vs completions by repair vendors
  3. Number of Load Discharge activities per Terminals
  4. EDI rejection analysis by Partners, by regions.

     

  1. Better Demand Forecast by single dashboard with predictive analysis:

  1. Booked commodities Vs Actual Shipments by container types/ by locations etc
  2. Booked empty containers Vs dispatched Containers by months/ by regions etc

     

  1. Mobility :

    Dashboard / Report / Visualization Interactivity on iOS/Android Mobile.

These high level reports can be customized with few clicks on tools like tableau/qlikview with further search parameters or can be drilled down to more detailed levels within seconds. Also deployment is faster than traditional BI reports.

You can refer to the comparative studies on different advanced tools and choose your own as per the suitability of your organization.

But finally, all shipping companies, using static reports, may experience a complete revolution once they start using advanced tools. It helps to improve operational efficiency, profitability and inventory utilizations by quickly providing trend in most understandable manner. These are also user friendly; simple drag and drop options for customization purpose give more power to users without having dependency on developers all the time. Users can easily transform from an ordinary biz analyst to a data champion and the enterprises can remain way ahead than other competitors.

References:

https://www.tableau.com/solutions/gallery/shipment-analysis



October 30, 2016

Pragmatic Data Quality Approach for a Data Lake

Posted by Ketan Puri (View Profile | View All Posts) at 4:27 AM

On 26th Oct 2016, we have presented our thought paper at the PPDM conference hosted in Calgary Telus Spark science center(Calgary Data Management Symposium, Tradeshow & AGM)

http://dl.ppdm.org/dl/1830

Abstract:

With the increase in amount of data produced from sensors, devices, interactions and transactions,
ensuring ongoing data quality is a significant task and concern for most E&P companies. As a result, most of the systems that are sources of data have deferred the task of data clean-up and quality improvement to the point of usage. Within the Big Data world, the concept of Data Lake which allows ingesting all type of data from source systems without worrying about the type or quality of data, further complicates the aspect of data quality as the data structure and usage is left to the consumer. Without a consistent governance framework and set of common rules for data quality, Data Lake may quickly end up into a Data Swamp. This paper examines the important aspects of data quality within Upstream Big Data context, and proposes a balanced approach for data quality assurance across data ingestion and data usage, to improve data confidence and readiness for downstream analytical efforts.
 

The key points/messages that we presented were,

1. Data quality is NOT about transforming or cleansing the data to fit into the perspectives...instead  it's about putting right perspective to the data....


2. Data by itself is not Good or Bad it's just data, pure in its most granular form


3. Quality is determined by the perspective through which we look at the same data


4. Architectural approach to abstract data from the perspectives or standards and build a layer of semantics to view the same data from different point of views. We don't need to populate data into models (PPDM, PODs etc.) instead we put models on top of the existing data promoting the paradigm of "ME and WE" where each consumer of the data has their view point of the same data. The concept of the WELL can be viewed in reference to Completion, Production, Exploration etc. without duplicating the data in the data lake.


5. Deliver quick value to the business and build their trust on the data in the data lake scenario


Please refer to the below link for the details

http://dl.ppdm.org/dl/1830