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Talk of blockchain technology is everywhere, it seems — but what is it, and what does it do? 1. Don’t call it “the” blockchain The first thing to know about the blockchain is, there isn’t one: there are many. Blockchains are distributed, tamper-proof public ledgers of transactions. The most well-known is the record of bitcoin transactions, but in addition to tracking cryptocurrencies, blockchains are being used to record loans, stock transfers, contracts, healthcare data and even votes. 2. Security, transparency: the network’s run by us There’s no central authority in a blockchain system: Participating computers exchange transactions for inclusion in the ledger they share over a peer-to-peer network. Each node in the chain keeps a copy of the ledger, and can trust others’ copies of it because of the way they are signed. Periodically, they wrap up the latest transactions in a new block of data to be added to the chain. Alongside the transaction data, each block contains a computational “hash” of itself and of the previous block in the chain. Hashes, or digests, are short digital representations of larger chunks of data. Modifying or faking a transaction in an earlier block would change its hash, requiring that the hashes embedded in it and all subsequent blocks be recalculated to hide the change. That would be extremely difficult to do before all the honest actors added new, legitimate transactions — which reference the previous hashes — to the end of the chain. 3. Big business is taking an interest in blockchain technology Blockchain technology was originally something talked about by anti-establishment figures seeking independence from central control, but it’s fast becoming part of the establishment: Companies such as IBM and Microsoft are selling it, and major banks and stock exchanges are buying. 4. No third party in between Because the computers making up a blockchain system contribute to the content of the ledger and guarantee its integrity, there is no need for a middleman or trusted third-party agency to maintain the database. That’s one of the things attracting banks and trading exchanges to the technology — but it’s also proving a stumbling block for bitcoin as traffic scales. The total computing power devoted to processing bitcoin is said to exceed that of the world’s fastest 500 supercomputers combined, but last month, the volume of bitcoin transactions was so great that the network was taking up to 30 minutes to confirm that some of them had been included in the ledger. On the other hand, it typically only takes a few seconds to confirm credit card transactions, which do rely on a central authority between payer and payee. 5. Programmable money One of the more interesting uses for blockchains is for storing a record not of what happened in the past, but of what should happen in the future. Organizations including the Ethereum Foundation are using blockchain technology to store and process “smart contracts,” executed by the network of computers participating in the blockchain on a pay-as-you-go basis. They can respond to transactions by gathering, storing or transmitting information or transferring whatever digital currency the blockchain deals in. The immutability of the contracts is guaranteed by the blockchain in which they are stored.

You don’t need us to tell you that the data world – and everything it touches, which is, like, everything – is changing rapidly. These trends are driving the opportunities that will fuel your career adventure over these next few years. At the heart of these trends is a massive wave of data being generated and collected by organizations worldwide. With this data we can shift our focus as analysts from explaining the past to predicting the future. And in order to do this, we need to spend less time doing the same things over and over and more time doing brand new things. And accomplishing all these changes will require us to work together differently than we do now 1. Bigger, Larger and Faster Data You’ve probably already heard the fact that every two years we, as humans, are doubling the amount of data in the world. This literally exponential growth of data is impacting analysis in some big ways: 2. Predictive Analytics The vast majority of time spent by the vast majority of today’s analysts is on understanding data collected in the past, often in the form of reports and dashboards. Those days are coming to an end. The data and tools now available are allowing analysts to go beyond just convincing someone to do something and instead to often just do it themselves. For example: 3. Automation Of Tasks Once upon a time, analysts built a model in Excel, and once a month or so, they exported the model to PowerPoint and send it to (or even printed it out for) the managers who relied on regular reports. Soon, there were too many reports, so maybe they used macros in Excel to automate the creation of reports. Or maybe they were lucky enough to have a dashboard program that had some automation functionalities built in. The future promises even more than this:

As the internet has grown so has folks’ tendency to chat instead of talk. Whether it’s booking an appointment at your local paediatrician or asking your boyfriend if he wants to go out for some pasta, people like texting. This is what chatbots want to capitalize on – People’s aversion to talking on the phone. Companies all over the world have embraced chatbots with open arms. They bring along with them a sense of humanity that no IVR can match. One of the reasons is the complete lack of any voice based interaction. While those beautifully sounding ladies on the IVR systems might be speaking correct English (or any other language(s)), they speak it with such robotic emotions that the only thing the person on the other end of the phone wants to do , is strangle themselves (or the IVR lady). A lack of direct human interaction allow chatbots to be much more than a computer program. Employing the help of mankind’s greatest asset – Imagination, chatbots can (almost) substitute for a real live human person on the other end. How to build an Intelligent Chatbot using AI Computer Scientists from all over the world are doing great work in making sure that chatbots can really feel like talking to another live human. Conversational Artificial Intelligence (the field of study that chatbots fall under) has seen tremendous progress in the last few years. Tech giants like Facebook, Google and Apple are constantly coming up with both highly valuable research papers as well as publishing user apps that at the same time is both showing off the work they have done as well as training their chatbots to become even better. However, this huge praise that Chatbots have come to receive has also muddled up the market. There is a huge different between a generic chatbot that can only choose between a set of responses and intelligent chatbot which try to make sense of the user’s chats and respond accordingly. If you are a business, you need the latter but chances are the agency you hired to develop you a chatbot is trying to fool you by giving a very cleverly disguised version of a generic chatbot. Here are 8 things that your intelligent chatbot MUST have : 1. Chatbot Carry an Intelligent Conversation A conversation is much more than saying yes or no. Moreover, the longer a conversation the more complex it gets. To carry an intelligent dialogue, the bot must be able to maintain the context of the conversation at all times. It also has to understand that natural conversations don’t always progress linearly – the bot must be able to process an unexpected reply and adapt to changes in the course of the conversation. 2. Build Contextual Engagement A smart chatbot has to understand who it is chatting with. In order to provide a truly personal experience, the chatbot has to know about the user’s interests, attributes and personal information – then tailor the conversation to fit them. The bot needs to provide content, advice, and offers that exactly fit the user. If all the information is generic, it will be shallow, unengaging, and in many cases, not very useful. 3. Leveraging Real-Time Transaction Data.  Connected with the need for contextual engagement, an intelligent chatbot must be able to access real-time insights on transactions. Without real-time data access and analytics, the power of artificial intelligence (AI) and contextual advice (either human-based or with chatbots) is limited. 4. Chatbot Can Reuse Existing Content To have a meaningful impact, it is crucial for the chatbot to be able to access content created and maintained in digital repositories across all channels. From digital ‘brochureware’ to FAQs, rules and regulations, and rate information, bots must be able to access and leverage this insight in real-time. 5. Build Deep Knowledge To build engagement, a chatbot needs to be able to provide advice, not just balances. Personetics believes bots need to be purpose-built – with deep knowledge on issues important to the customer. With PayPal supporting payments through Facebook Messenger, the bar transactions through the bot channel has been set and is being raised. 6. Work Seamlessly Across Channels  Customers expect a consistent experience across the digital landscape – online, mobile app, Facebook Messenger, Amazon’s Alexa, etc. A bot can not be a silo, but should be able to traverse across and between multiple channels. This may be a challenge for organizations who still can not achieve this within internal channels (mobile, branch, online, call center). 7. Get Smarter Over Time  An intelligent chatbot must get to know customers better through over time as more conversations and transactions take place. It must improve based on how a customer reacts to information and advice provided by the chatbot over time. 8. Anticipate Customer Needs Almost half of all chatbots are only used once. This happens when a bot experience does not meet exceed expectations. To get customers in the habit of conversing with your chatbot, it needs to proactively reach out to customers with information, insight, and advice – presented at the right time and place based on predictive analysis of individual customer needs.

In this era when Data Science and AI are evolving quickly. Critical business decisions are being taken and strategies being built on the output from such algorithms, ensuring their efficacy becomes extremely important. When the majority of the time of any data science project is spent in data preprocessing it becomes extremely important to have clean data to work upon. As the old saying goes ‘Garbage in, Garbage out’, the outcome of these models is highly dependent on the nature of data fed in, hence data quality challenges in data science are becoming increasingly important. Challenges to Data Quality in Data Science Let’s understand this problem better using a case. Let’s say you are working for an Indian bank who wants to build a customer acquisition model for one of their products using ML. As with typical ML models, they need lots and lots of data and as the size of data increases, your problems with data also increases. While doing data prep for your model you might face quality challenges. Let’s look at a few of them one by one. The top most Common causes of data quality issues are: Duplicate Data: Suppose you are creating customer demographics variables for your model and you notice that there are a cluster of customers in your dataset that have exactly the same age, gender and pincode address, well this case is quite possible as there can be a bunch of people of the same age, gender living in the same Pincode, but you need to have a closer inspection at the customer details table and check if rest of the details(like mobile no, education, income, etc.) of these customers are also same or not. If they all are the same, it means it is probably due to data duplication. Multiple copies of the same records not only take a toll on computing and storing but also affects the outcome of the machine learning models by creating a bias. Inaccurate Data: Suppose you are working on location specific data, it can be quite possible that the pincode column you fetched contains some values which are not of 6 digits. This problem occurs due to Inaccurate data and it can impact your model where data needs to get aggregated at pincode level. Features with a high proportion of incorrect data should be dropped altogether from your dataset. Missing Data: There can be data points which might not be available for your entire customer base. Suppose your Bank started to capture the salary of your customers in the last one year only, customers who are associated with the bank for more than one year will not have their salary details captured. However important you might think this variable can be for your model, if it is not available for more than 50% of your entire dataset, it cannot be used in its current form. Outliers: Machine learning algorithms are sensitive to the range and distribution of attribute values. Data outliers can spoil and mislead the training process resulting in longer training times, less accurate models and ultimately poorer results.Correct outlier treatment can be the difference between accurate and an average performing model.  Bias in Data: Bias error occurs when your training dataset does not reflect the realities of the environment in which a model will run. Let’s understand this in our case, typically in acquisition models the potential customers on which your model will run and predict in future can be of two types, Credit experienced or new to credit. If your training data contains only credit experienced customers, your data will be biased and will fail miserably in the production settings as all the features which capture customers performance using the credit history(Bureau data) will not be present for new to credit customers. Your model might perform very well on experienced customers but will fail for the new. ML models are as good as data they are trained on, if the training data has systematic bias your model will also produce biased results. How To Address Now that we understand the data quality challenges, now lets see how we can tackle them and improve our data quality. But before going further let’s first understand that it is certain that data will never be 100% perfect. There will always be inconsistencies through human error, machine error or through sheer complexity due to the growing volume of data. While developing ML models there are few techniques that we can use to address these issues like: Apart from these techniques we can also add some logical rule based checks to validate the data if it reflects the real value with the help of domain experts. Also there exists a lot of software solutions in the market to manage and improve data quality in data science and help you create better machine learning solutions. Final Words Dirty data is the single greatest threat to success with analytics and machine learning and can be the result of duplicate data, human error, and nonstandard formats, just to name a few factors. The quality demands of machine learning are steep, and bad data can backfire twice — first when training predictive models and second in the new data used by that model to inform future decisions. When 70% to 80% time of a data scientist in any ML project is spent in the data preparation phase then ensuring that high-quality data is being fed into ML algorithms should be of the highest importance. As by each passing day, more and more data is being generated and captured, addressing this challenge right now is more important than ever. 

Business leaders and stakeholders often think about right time to start looking at analytics and sometimes fall shy due to concerns surrounding data availability, quality of data, lack of resources and value of the overall exercise. We have been asked quite a few questions ourselves in last couple of months by decision makers across Insurance industry. Frequent ones are quoted below with response Assumption 1: We just have few thousand records, i am not sure if this is enough for any kind of predictive analytics. That’s a valid observation, for any predictive model to be successful we need to build and validate it on sufficient dataset. Generally you can have a fairly good model for 1000 records and atleast 100 events. Example 100 lapse in 1000 observed customers. As a thumb rule in addition to above point for each variable used for prediction there should be at least 20 records.Ex if 10 variables are used for prediction, minimum no of records expected are 10*20 i.e. 200. This whole process can help you identify deficiencies in data collection process, like missing values, invalid data or some additional variable should have been collected. Such interventions at early stage can be very helpful and can go a long way in improving data quality. Assumption 2: Our data quality is too bad, I don’t think we can do it right now Addressing data quality is core to the process of modeling. Data once imported is processed to bring it into meaningful shape to proceed for any further analytics. Availability of high computing power at lesser cost makes sure any size of data is small nowadays and can be processed in lower time and cost. Assumption 3: I am not too sure on the Return On Analytics The real fruit of analytics is not just in the scorecards or numbers but also in the way it is integrated and implemented within organization. Having an list of customers in excel scored on basis of lapsation might not be much useful but if it’s real time and integrated across IT ecosystem of web or mobile giving your agents, Customer Service team insights into consumer behavior every time he interacts with your firm, it becomes much more actionable. Think about product affinity ratings for customer integrated with Tablet app agents carry these days. Not only your agent will be able to push right product to the customer based on his needs but importantly build a long term relationship. Assumption 4: I already have basic predictive modeling initiatives running but not very effective. What more can I do! Basic premise of any analytics initiative is framing the right question, having the right data at hand and finally a strong actionable strategy. Doing this right will definitely result in good show. Once you have considered looking at internal data sources, you can also try adding external data sources like CIBIL, Social Media and economic indicators like Inflation, Exchange rate etc to glean information about financial behavior, consumer life style and events. Frame hypothesis which you would want to validate against external data sources and test them.

Organizations around the world strive to achieve profitability in their business. To become more profitable, it is essential to satisfy the needs of customers. But, when variations exist between individual customers how they can effectively do that. The answer is- by recognizing these differences and differentiating the customers into different segments. But how do organizations segment their customers? And in this article we’ll help you understand this from a data science perspective. What is customer segmentation? Customer segmentation is the process of dividing the customer base into different segments where Each segment represents a group of customers who have common characteristics and similar interests. As explained above, the exercise of customer segmentation is done to better understand the needs of the customer and deliver targeted products/services/content. With time, all sorts of organizations from e-commerce to pharmaceutical to digital marketing have recognized the importance of customer segmentation and are using it improve customer profitability. Customer segmentation can be carried out on the basis of various traits. These include : How to perform customer segmentation? Start with – Identifying the problem statement One of the foremost steps is to identify the need for the segmentation exercise. The problem statement and the output expectation will guide the process of segmentation. Example: In both the cases, the intent or need to perform customer segmentation is different. This will further determine the approach taken to achieve desired outcome. Gathering data Next step is to have the right data for the analysis. Data can come from different sources- internal database of the company or surveys and other campaigns. Other third party platforms like Google, Facebook, Instagram have advanced analytics capabilities to allow capture of behavioral and psychographic data of customers. Creating the customer segments Once you have defined problem statement, and gathered all the required data for it, the next step is to carry out the segmentation exercise. Key steps here will be: Data science and statistical analysis with the help of machine learning tools help organizations deal with large customer databases and apply segmentation techniques. Clustering, a data science method, is a good fit for customer segmentation in most of the cases. Usage of the right clustering algorithm depends on which type of clustering you want. Many algorithms use similarity or distance measures between data points in the feature space in an effort to discover dense regions of observations. Some of the widely used machine learning clustering algorithms are : Segmentation backed by data science helps organisations to forge a deeper relation with their customers.  It helps them to take informed retention decisions, build new features, and strategically positioning their product in the market.

“Analytics is a journey and not a destination!! It takes considerable effort to frame that journey and execute it with a sense of purpose. You will encounter stumbling blocks that may threaten your initiative but you need to find a way out and keep marching ahead.” How is it like to build a data analytics strategy? We did a data analytics exercise for a US client recently in education domain that had all the flavors of roadblocks one can encounter on venturing into analytics territory. I intend to summarize those here along with solutions we found in collaboration with all stakeholders Takeaways This was just a month’s exercise. Surely we will hit many such scenarios ahead.

AI has received a lot of hype in the marketing community — and for good reason, too. As research and advisory firm Forrester Research notes in its report, “AI Must Learn the Basics Before It Can Transform Marketing,” AI-powered marketing applications promise numerous benefits, including efficiency and speed, smarter decision making, and optimized customer journeys and campaign performance. But this hype, or in some cases overhype, has caused some confusion within the marketing industry. Here are five Artificial Intelligence myths outlined in the report, and the truths behind them. Myth 1: AI Is New AI has actually been around for decades. John McCarthy, who’s been credited with coining the term, wrote a proposal on the subject matter back in the mid 1950s. The concept isn’t new to marketers, either. Joe Stanhope — Forrester VP and principal analyst — says companies like Rocket Fuel and MediaMath have leveraged AI for a while to optimize the purchasing of display ads. The media has been following the rise of AI, too. And as AI was brought into the limelight — winning Jeopardy matches, mastering the board game Go, serving as a main character in movies — people’s ideas of AI, and its originality, were altered. “People have in the back of their heads these preconceived notions of AI because they’ve grown up with it kind of in the background,” says Stanhope, who also authored the report. Myth 2: It’s About Complex Math and Algorithm While it can be easy for those without a PhD in mathematics to feel intimated by AI — and it is highly complex “under the hood” — Stanhope says the technology is more about the ingested data. “We tend to think about heavy-duty math and algorithms,” he says, “but, in fact, AI is really a data play.” Indeed, the report says marketers need to provide their AI systems with “accurate, updated, and complete data” for the technology to detect connections. They also need to establish a feedback loop, the report notes, to drive optimized results. Myth 3: AI Systems Work Instantaneously Stanhope compares AI systems to human babies: Both don’t know much in the beginning and need proper training to flourish. Indeed, just as how babies need time to learn how to walk and talk, AI systems need time to ingest companies’ data and key performance metrics and understand business problems. Myth 4: AI Will  Put Marketers Out Of Work While Forrester did predict that cognitive technologies like AI would replace 7% of U.S. jobs by 2025, Stanhope says he doesn’t expect marketers to completely turn over their jobs to computers anytime soon. “We do not see AI as a system that’s going to put marketers out of work,” he says. On the contrary, Stanhope says companies will still need marketers to input the necessary data and monitor the technology to ensure that it’s meeting KPIs and avoiding unintended consequences. He also expects companies to put a premium on creativity and content creation so that the machines will have enough variants to test. AI will simply ingest, analyze, and act on the data at velocities and speeds with which humans cannot compete, notes the report. In other words, it’s not a set-it-and-forget-it kind of technology. “Humans are still very much involved in this,” Stanhope says. “It’s a human-computer relationship. It’s very symbiotic.” Myth 5: AI Will Help Marketers Uncover Rich Insights About Their Customers AI systems aren’t customer insight solutions, Stanhope says. That’s because they’re powered by the customer data companies have. Indeed, Stanhope says AI systems are designed to optimize outcomes, not simply tell marketers what their customers like and dislike. “It’s not an AI system’s job to teach marketers about their customers,” he says. The report also notes that AI systems process so much data at such high speeds that marketers cannot expect a “play-by-play” of what it learns.

Undoubtedly, Artificial Intelligence (AI) can offer organizations a substantial competitive advantage if used in the right place and in the right circumstances. There is also considerable pressure on organizations to go the AI route for fear of losing an edge to competitors. This pressure can easily be felt by business leaders who need to craft and implement enterprise AI strategy. In a recently conducted survey (from Oct’17-Nov’17), majority of business leaders indicated Machine Learning (ML) and AI as their companies’ most significant data initiative for next year. 88 percent of respondents indicated that their company already has, or has plans to, implement AI and ML technologies within their organization. But, it is still not clear whether AI would bring productivity benefits or not, or whether they will have any impact on an organization’s revenues. So, what should organizations be thinking about? What questions company leaders should be asking before they push forward? Here are 11 key questions answers of which they need to find out: 1. Do you really need AI to solve this problem? Some automation and analytics use cases are simple enough that they can be solved with much simpler procedural code rather than building and maintaining an AI model. Enterprises need to figure out what they are trying to do and decide if AI is worth the investment. 2. How will AI improve your Customer Engagement? Businesses should leverage AI to deliver the right message at right time for right customer to significantly improve the customer engagement. By identifying low hanging fruit, high impact opportunities they can transform their brand for optimal customer engagement and make immediate and tangible difference in customer relevance. If cost reduction is an important driver, think about AI-powered chatbots to reduce mundane customer service tasks. 3. What is the organisation’s business case? If AI is deployed simply as a experiment without identifying and solving a specific business problem, then it would turn out to be “short-lived-no business value proposition” as leadership will not see any return on investment and people will simply stop using it and the entire technology will be dismissed as “not working for us.” 4. Do you have the necessary data? This is really a significant and important factor to be taken care of. Using AI involves being able to train a model on data. So, companies planning to use AI in coming future would really need to start thinking about data collection now without which AI will not be as effective. Enterprises need to understand the fact that AI is only as good as your data and goals allow it to be. Without framing a robust key performance indicator (KPI) and performance targets you would find yourself lost in corpus of data and wouldn’t understand the right way to optimize your actions to achieve desired results. 5. Do You Trust The Data Sources AI Will Use? One of the key questions that organizations need to answer is whether their data and date sources are suitable for AI. They should view data as a strategic business advantage and give attention about the data they’re collecting, how they’re storing it and how they can use it to create a personalised experience for their customers. 6. Is Your Data Architecture Suitable? While data is important, it is not enough. Organizations need to build a robust data strategy and ensure they have the right and effective data architecture in place. 7. Can Existing Data Management Systems Support AI? Can the existing data management systems hold up under the new load of artificial intelligence. AI systems use data as fuel and to have an effective AI model, this data shouldn’t be incomplete, inaccurate, or biased. That said, don’t wait for the data to be perfect — current AI is perfectly capable of determining what data works and what is too unreliable to use. 8. What Are the Consequences of Getting It Wrong? Sometimes, AI is all about statistics and finding the right correlation. In such cases, similar to humans, AI might produce wrong results depending on the data quality. So, business leaders need to think whether they want to implement AI in a process with a lot of variability that may have a lower accuracy rate and could have major consequences after getting it wrong. 9. What Are the Risks? AI comes in two distinct flavors — transparent and opaque, and both have very different uses, applications, and impacts for businesses and users in general. In some instances, businesses will need to employ a transparent form of AI that will explain the logic and exactly how they reach certain algorithmic-based decisions. 10. How Will This Impact Workers in Your Organization? AI’s rapid business adoption is expected to replace part of function of an employee’s role which might develop a negative perception and resistance for this change. Sustained AI adoption would require business leaders to involve employees and their line managers right from the start. Effective learning programs would help them take this change in a positive manner. 11. Will AI Integrate With Your Current Stack? AI solution should be integrated as part of a broader process not as standalone technology solution. AI, process and people should work together to make the business ecosystem more efficient and enhance the productivity, results and revenue.

Given the availability of a huge data pool, the manufacturing industry has started optimising areas that have the most impact on production activities with a data-driven approach. With access to real-time shop floor data manufacturing companies have the capability to conduct sophisticated statistical analysis using big data analytics and machine learning algorithms to find new business models, fine-tune product quality, optimise operations, uncover important insights and make smarter business decisions. In manufacturing, Machine Learning and Big Data techniques are applied to analyse large data sets for developing approximations regarding the future behavior of the systems, detect anomalies and identify scenarios for all possible situations. In this blog article, we take a look at how big data analytics and machine learning are transforming the manufacturing sector. Predictive Maintenance It is well understood that maintenance done at the right time reduces costs. One of the most impactful applications of Machine Learning in manufacturing has been that of predictive maintenance. The Industrial Internet of Things (IIoT) market is standing at an estimated $11 trillion and predictive maintenance can help companies save almost USD$ 630 billion over the course of the next 15 years. Machine Learning can provide valuable insights into the health of the machines and predict if a machine is going to experience a breakdown. This information can help companies take preventive measures instead of reactive measure and shorten unplanned downtime, excess costs and long term damage to the machine. Enterprises can leverage Machine Learning algorithms to analyse sensor data to improve the Overall Equipment Effectiveness (OEE) by improving equipment quality and the entire product line along with boosting shop floor and plant effectiveness. Preventive Maintenance or Condition Based Monitoring Given that manufacturing enterprises have a large install base of machines, they need to ensure that machinery does not break down when they need it most. With Preventive Maintenance or Condition Based Monitoring, they try to maintain the equipment in optimum working condition and prevent any unplanned downtime by detecting equipment failures before they happen and fixing them within stipulated time. Preventive Maintenance is a process of continuous machine monitoring where by using pre-defined parameters, patterns that indicate equipment failure can be tracked and machine failure predictions can be made on time. Condition Monitoring ensures that an equipment is running or is maintained by iterating parameter variances that are constantly monitored.. Quality Control In today’s regulatory landscape product quality is of paramount importance. Most manufacturers say product quality defines their success in the eyes of their customers. They constantly seek ways to reduce waste and variability in their production processes to improve efficiency and product quality. Leveraging principles of advanced big data analytics and machine learning concepts, manufacturing companies can capture sensor data from shop floor tools and equipment to take an increasingly granular and enterprise-wide approach to quality control. In addition, manufacturers will also be able to identify defects, uncover the root cause of problems, reduce the risk of shipping non-conforming parts, enable engineering improvements and determine which factors, processes, and workflows impact quality. Effective Supply Chain Management McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. Big data analytics and machine learning algorithms can help manufacturing companies assess the state of the supply chain and drive efficiencies with inventory optimization, demand planning, supply planning, operations planning, logistics etc. This allows manufacturers and suppliers to partner in a real-time environment to prevent keeping safety stocks levels high, adjust inventory positions to ensure that the right inventory is positioned at the right location to service customers better and prevent stock-outs, and also improve transportation logistics. Optimization of Operations A Gartner survey on the projected use of manufacturing analytics over next two years showed that 88% of companies plan on utilising data metrics to improve manufacturing responsiveness, 81% to improve capacity utilisation, 74% to understand their true costs, and 75% to make faster and better decisions. By making real-time adjustments manufacturing companies can optimise the operational efficiency of manufacturing assets. This involves managing production capacity by having a real-time view of equipment performance and production processes, along with identifying assets locations, including those of products and people. By leveraging machine learning and advanced analytics enterprises can assess demand forecasts and other parameters such as future raw material costs, the cost of manufacturing and distribution, working capital analysis etc. It directly helps in improving the quality of Sales and Operations Planning by supply chain network optimisation. Improvement of After Sales Service Manufacturers are coming to understand that their actions after making sales are as important as the efforts they put into preparing for the sale, which all has an increasingly significant impact on their company’s financial performance. A recent study found that 27% of manufacturing companies’ total revenues came from service. Another report suggested that an average gross margin of 39% could be attributed to after-sales service. Undeniably, high-quality service is important to obtaining financial success. Therefore,manufacturers will move away from using outdated technologies and business practices for inventory and after-sales management that provide little visibility and control. They will need to utilise predictive analytics to optimise their after-sales service and product parts business performance to improve customer loyalty, save time, and reduce costs. Customisation of Products The power shift from manufacturers to consumers described earlier is also driving investment in product customisation capabilities that are largely made possible by advancements in big data usage, machine learning and advanced analytics. When manufacturers provide tailored products, consumers provide extensive data about their preferences and behaviours that manufacturers can use to inform future product development. Big data analytics then allows companies to analyse customer behavior and develop methods of delivering products in the most timely and efficient way possible. We will thus see manufacturers moving data out of silos and creating a data ocean of customer information with the goal of becoming more agile and responsive in making products to individual requirements both in B2C and B2B environments. This contrasts with the traditional focus on