Chatbots and What They Can Do in the Manufacturing Industry

Chatbots and What They Can Do in the Manufacturing Industry

In recent years the development of chatbots and conversational AI has progressed extremely rapidly. What was originally used primarily for B2C customer interactions is today used in an interesting variety of applications.

In the manufacturing industry, a chatbot can serve two functions. Obviously, the first one would be the traditional role of assisting customers or clients to find solutions to their questions and queries. However, more and more manufacturing companies are trying to deploy conversational AI to support employees in getting accurate information regarding production processes to make the whole operation more efficient

Maybe it would be ideal to highlight some use cases in the manufacturing industry and how Conversational AI has been used to improve efficiency and lead to customer delight.

Supplies and inventory check

No manufacturing operation can survive without the regular supplies of spare parts and raw materials. Companies in the manufacturing sector need to keep track of vendor supplies and existing inventory in order to avoid an unnecessary build-up of raw materials and to make sure that orders are fulfilled. Thanks to manufacturing NLP chatbots, employees can access the status of supplies and inventory and any relevant information including supply lead time whenever they need it.

In addition, ERP solutions or other systems can be integrated in order to access information even faster. Businesses can also train the algorithm to analyze how raw materials are used and thereby prevent any wastage. A bonus is that employees can follow up on orders via the same interface.


Handling floor queries

Manufacturing plants are typically a hub of activity, and factories are spread over several floors or halls. This can result in some confusion and delays especially if employees have to shift between different floors or sites to complete a task. A chatbot can handle floor queries and help avoid unwanted surprises and a waste of time and effort. This is sometimes referred to as knowledge management.

It can also be used to check the distribution of workload on different floors, the production capability at a point in time, and observe what kinds of maintenance issues are recorded. These requests normally take a lot of time to address, but with a chatbot, one would be able to get the information quicker than with standard monitoring systems. These AI-powered assistants also become a crucial part of quality control by providing the manager with in-depth insights about procedures and the amount of attention they need.

 

Updates and delivery notifications

A manufacturing company has to deal with a lot of supplies and deliveries. With a chatbot, everyone from employees to buyers can be notified on any changes in the status quo and immediately they occur. The chatbot will be able to give information about the status; time and condition of the delivery, saving buyers and the customer support team a lot of time. Overall, intelligent assistants can automate key business processes and ensure that tasks are being handled more efficiently.


Implementing a chatbot for your manufacturing business provides a lot of advantages on multiple levels, including: 

Improving operations

The biggest benefit of implementing a chatbot is its availability. In fact, a chatbot is always available at any given hour and any day, unlike a human representative. Moreover, a chatbot can answer an endless number of questions without any ennui or waiting time. This availability makes operations and processes run smoothly while avoiding friction between the major stakeholders, which overall, directly affects the business and improves its performance.

Providing an interactive platform

Chatbots are an effective way of communicating because they do not deliver a passive experience to the user but rather an engaging interactive conversation that helps them in their queries and makes them feel cared for. Chatbots make it possible to increase engagement rates and attract more and more potential customers.

Improving organization efficiency

Intelligent virtual assistants help companies in many ways; one way is improving their overall efficiency and productivity. With a chatbot ready to take over standard and redundant tasks, the company finds itself with more resources both in terms of manpower and finances to tackle the operations that require human intervention.

Additionally, a chatbot can keep track of various metrics by integrating them with existing systems ensuring optimal efficiency and better supply chain management. This results in the firm having the optimal number of manpower and inventory at all points in time.

While the use cases and the benefits are quite obvious, many organizations continue to struggle with incorporating the technology. 


IoT as a driver for conversational AI in Manufacturing

The changing technology landscape of IoT is both imminent and rapid. Furthermore, as newer features and use cases are introduced, there is an added responsibility to educate the end-users which can be burdensome for both the users and the developers of the system. Complicated systems cause difficulties in adoption and diffusion. As an assistive technology, chatbots can simplify the learning curve in the following ways: 

a) Help Features: IoT-enabled Chatbots can feature help texts which clarify the user request to ensure that the action performed is the same as the one intended. 

b) Recommendations: Chatbots can recommend possible actions to the user which can be made more intelligent and context-aware depending on user preferences and the dynamics of the environment. 

c) Automation: Chatbots are good at automating common cyclic, tasks and can perform certain actions such as monitoring the availability of sensors (uptime, downtime, etc.) and others. 

As more use cases are discovered, chatbots can make the adoption and diffusion of IoT systems significantly easier and reduce the cognitive burden required to understand the functionalities of these systems.

Remedying hardware and software issues in modern consumer IoT systems can be an irksome task. The recourse is to call the service provider for technical support or in many cases returns the product. Either way, it is an unnecessary burden on the user as well as the support vendors in today’s cost structure. Smart Chatbots often have support services built into their functionality. They can even integrate human-in-the-loop systems to handle situations that the Chatbot is not trained or authorized to perform, in real-time. In this manner, users need not go beyond the scope of the chatbot application to look for product support. Any software issue or hardware malfunction can be monitored and Over the Air (OTA) software repairs can be performed. Chatbots can also be used to schedule technical repairs making them a convenient and fast solution for customer support. However, it is imperative that the entire AI system is aligned with the overall business objective. The standardization of processes across an organization and defining business rules is imperative. Moreover, with increasing digitization in enterprises, technology architecture has become a decisive factor for the successful adoption of any digital interface, including chatbots. It does not make sense to first invest in a particular technology platform to build a chatbot and then encounter a not-so-pleasant surprise of not being able to integrate it with existing ERP or other applications.

The key is to understand that chatbot development is a long journey. Enriching a chatbot with features takes time. There is a minor distinction between a conversational chatbot and a transactional chatbot. In most cases, the features may overlap over a period of time after multiple scope enhancements. 

For practical implementation purposes, it is simpler to look at chatbots as three types – basic, intermediate, and advanced. Progressing from a basic to an advanced chatbot is a journey spanning a substantial period of time, and involving continuous improvement and incremental enhancements in features.

Chatbots are a powerful mechanism that makes repetitive tasks more efficient and provides analytics and insights into employee references and the organization’s areas of improvement with such obvious advantages, investment in the deployment of chatbots makes eminent sense. Conversational Chatbots have their limitations, but many have already proven their worth. However, despite the strides in technology, it must be stated that it is not the only factor that defines efficient software. As with many new technologies, good experience, design and human behavioral science are crucial to successful design and implementation. However, training of conversational AI will get easier and faster over the years and therefore there is no doubt that it will have a long-life span. The ultimate objective of course would be a flawless experience with a conversational agent.

AI-Powered Recommendation Engines and Digital twins

AI-Powered Recommendation Engines and Digital twins

Imagine that you had a perfect digital copy of the physical world: a digital twin. This twin would enable you to collaborate virtually, intake sensor data and simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately…. Meanwhile many companies are using digital twins in a variety of ways.  For example, the automotive and aerospace industry are using digital twins as an essential tool for optimizing entire manufacturing value chains and innovating new products. And Singapore as a pioneer of smart city projects, uses a detailed virtual model of itself in urban planning, maintenance, and disaster readiness projects.

It is estimated that the market for digital twins, which was worth US$3.8 billion in 2019, will reach US$35.8 billion in value by 2025.

What accounts for this kind of growth? And why now? Since more than 20 years pioneering companies have explored ways to use digital models to improve their products and processes. While the potential of digital twins was clear even then, many other companies found that the connectivity, computing, data storage, and bandwidth required to process massive volumes of data involved in creating digital twins made them not economically viable.

The digital twins trend is gaining momentum thanks to rapidly evolving simulation and modeling capabilities, better interoperability and IoT sensors, and more availability of tools and computing infrastructure.

As a result digital twins and Artificial Intelligence have grown rapidly and are considered to be key enablers for Industry 4.0. As a digital representation of a physical entity, it helps businesses make model-driven decisions. The base of digital twins and AI in industrial sectors is extremely dependent on the systematic and in-depth integration of domain-specific expertise. Simulations without digital twins are expensive so data scientists use data to develop models that replicate real-world assets in the digital space. IoT sensors, log files, and other relevant information are used by digital twins to collect real-time data for the accurate modeling of assets. In manufacturing, the digital twins are a virtual representation of the as-designed, as-built, and as-maintained physical product. It is augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment.

How the digital twins is being implemented today

Asset Lifecycle Management (ALM) is the major focus area where digital twins have been implemented. Asset maintenance is an extremely time-consuming and expensive task. However, today technologies like augmented reality can access virtual engineering models that can be covered over physical equipment performing maintenance using specialized AR goggles. This enables maintenance technicians to use the most accurate data engineering ensuring the right maintenance and performance specifications are executed efficiently. These same maintenance methods can be applied to machines, work cells, and factory production systems. Today, advanced virtual simulation technology is an integral element of the digital twins. Simulation platforms can simulate and validate the functionality of product design simultaneously allowing designers to validate their designs.

Different ways digital twins are used to improve manufacturing operations:

Product Design
During the design phase, digital twins can be a virtual prototype and is adjusted to test various simulations/designs before funding a solid prototype. Digital twins allow manufacturers and engineers to test designs under any circumstances to create a perfect product. This helps to save time and cost by lowering the number of iterations to get the product into production without any errors.

Process Optimization
To create digital twins of the process and analyze performance indicators, IoT sensors can be used on a manufacturing line. Digital twins help to improve the existing manufacturing process by using advanced simulations based on real-world data given by sensors. Using historical and real-time data, AI can simulate the entire process and identify areas that can be improved.  This helps to identify new ways to optimize production, improve production practices, reduce variances, and perform root-cause analysis. 

Quality Management
You can monitor what is going on in real-time with the help of IoT sensors across your production line to maintain top-quality production and to eliminate rework. Digital twins can simulate every part of the production process to identify errors and test different materials.

Supply Chain Management
Supply chain management has a tremendous impact on business success. Delivering products to the consumer on time is the most important aspect of the manufacturing industry.  Currently, most manufacturers rely on distribution firms to ensure on-time delivery. These distribution firms are also dependent on digital twins to track, analyze key performance indicators and improve performance. This helps production and distribution to reduce warehousing costs and increase customer satisfaction.

Predictive Maintenance
Digital twins can be used to run simulations and predict how machines perform in the future. Manufacturers can use AI and digital twins to identify the date when a machine will crash. This will help to reduce downtime, organize maintenance, and lower repair costs. The need for preventative repairs or maintenance before a severe problem occurs is most conducive to smooth operations.

Enhance Customer Experience
Digital twins collect data over time that give insights that serve to improve performance, management end-user experience, and distribution. Digital twins help understand how products are performing in the real world and how customers are using them. The data can also be used to improve customer experience and customize the product according to customer expectations which can be achieved only with digital twins technology.

Digital twins – the future of manufacturing

Digital twins use multiple technologies like AI, machine learning, and augmented reality that helps to gain information on how to improve business processes.  It gives the ability to connect numerous machines, processes, and solutions into one working system. Digital twins generate highly valuable insights that help your organization to grow and meet industry challenges.

Imagine a situation where a manufacturer has newly set up a plant to manufacture cold drinks. He is very aware of the fact that the market he is catering to is highly segmented, volatile, and subject to seasonality.

He needs to find answers to the following questions:

  1. Who is his target customer and in which pockets of the city is he/she concentrated?
  2. What are these customers preferences in terms of flavor, pack size, price etc?
  3. What is likely to be the offtake/area/ day? across the year?
  4. What is the ideal balance of production volume to offtake and raw material purchase?

In times gone by, it took years of experience and costly mistakes to arrive at these answers. Today a simulation of the market and the production facility can provide the answers sometimes literally in minutes.

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External Data: Add a New Dimension to Business Decisions

External Data: Add a New Dimension to Business Decisions

External data can be transformative

Organisations no longer operate as a standalone entity, and instead are part of networks comprising suppliers, resellers, channel partners, regulators, and other stakeholders. Analysing external data can point out the risks, opportunities and trends that firms would miss if they relied on data generated from internal operations, customers, and first-tier suppliers.

Relying solely on internally generated information can leave gaps, and as organisations realise this, they are increasingly moving to incorporate new, non-traditional sources of data that sits outside their systems. The challenge however is analysing the tonnes of data being gathered and stored at an exponential pace. According to a study, the data stored in data centres will grow almost five-fold to reach 1.3 zettabytes globally by 2021.

An MIT Sloan Management Review report found that the firms making the most innovative use of data and analytics were more likely than others to leverage more external data sources, including social, mobile, and publicly available data.

Why external data must be a part of your data strategy

External data gives a bigger picture. Collecting, evaluating, and analysing external data – such as user generated data, public data, competitor data etc – gives business leaders the full view.

It is not expensive. Today several tools, both paid and open source are available, to make sure it does not cost much to source external data. Data from government organisations, the news, social media and other online and broadcast media is even available for free.

Real insights with external data analytics. External data analytics can have a major impact when it comes to making decisions about the future of a business. Organisations can personalise marketing offers, improve HR decisions, build new revenue streams by launching new products or services, improve risk visibility and mitigation, and better anticipate shifts in demand. For instance, investment firms can use third-party data to build models that could predict the best types of customers to target in marketing campaigns. External data can help train the models to identify potential targets that fit profiles similar to the most engaged customers, thus optimise marketing spend. Several start-ups monitor social networking data to predict customer patterns and employee sentiment.

External data helps a business stay competitive. With competition for the customer’s wallet share being at an all-time high, the ability to quickly and regularly keep track of what the competition is doing is invaluable. Organisations can also predict trends and spot patterns that will make them more relevant to customers.

Add real-world context to decision-making. Organisations must gauge and predict the impact of external events – such as shifts in global purchasing trends, pandemics, marketing campaigns and so on – and guide product/ service decisions.

Tap into a data ecosystem

Unfortunately, as studies indicate, most organisations have not yet built in-house capabilities to put third-party data to good use. This would involve identifying, evaluating, procuring, and preparing external data consistently, and designing a continuous process to identify, engage with, and evaluate new external data sources. They would also need to regularly engage with partners and fuse these data sources with analytics processes or product offerings, as well as internal data.
It may help to be a part of a larger data ecosystem which involved multiple entities that directly or indirectly consume, produce, or provide data and other related resources. Organisations can create a cross-functional group as an interface to the wider data ecosystem in order to draw on competencies from multiple areas, such as product management, business analysis, data science, legal, and procurement, to address organizational and technical challenges related to third-party data. They can also create specific roles – termed ‘data curator’ by Gartner – focused on handling third-party data and related requests. Curators can keep data requests and sources up to date, while also ensuring quality and accuracy of data.

Connecting to a data ecosystem

We can categorize data services according to the level of insight they provide, as detailed here:

Simple data services. Data brokers gather data from a variety of sources. The conditioned data they provide serves as an additional input to the decision process, be it for a human user or device.

Smart data services. Analytical rules and calculations are used to enhance the data and present it as scores or tagging of objects.

Adaptive data services. Specific analytical requests from customers are catered to by combining third-party data with data from other sources.

Other ways to segment data services include specialists in domains, such as hedge funds or health care providers.; consulting and systems integration services providers who cater to demands for new insights from publicly available data and other external data, in addition to custom analysis.

The challenges of using external data

Access to external data is getting easier in some ways, but it can still be daunting. Organizations report a wide variety of business and technical challenges in deriving insights from external data. Among the business challenges are the size and complexity of the data-provider market, which can make it hard to identify the right data sources and partners. Negotiating acquisition of data can be arduous, depending on factors such as:

  • Ongoing access to data for refreshing machine learning models
  • Usage restrictions
  • Revenue share demand from the data vendor
  • Liability if the data proves to be inaccurate or tainted

This process can involve lengthy risk and legal reviews of vendor contracts and licensing agreements. The ongoing management of a growing roster of data-sharing relationships and partnerships can be taxing as well.

Third-party data can bring lots of opportunity, but applying it for real results can be challenging.

Even before we consider the technical challenges that hamper the deriving of insights from external data, it can be challenging to identify the right data sources and partners. There are several factors involved such as updating data, usage restrictions, revenue share demands from the vendor, liability of inaccurate or tainted data and so on.

Technical challenges include essentials such as measuring data quality and filtering out inaccuracies. Data pre-processing, such as cleansing and formatting it for analysis, takes a lot of time. Once you have sorted out good quality data, cataloguing it and keeping it secure is the next hard task especially if you have systems originally designed to manage only internal data.

Few organizations have standardized procedures to deal with external data and even fewer utilize external data to its full potential. As internal data analysis teams are less familiar with external data, it might take them a bit longer to understand external data, simply because it’s more complex and quite different from the internal data they are used to compiling and evaluating. Which also means that the data team will have to learn how to package and interpret external data and apply it so that they extract relevant answers to business questions.
Several studies indicate that third-party data is riddled with inaccuracies as well as inconsistencies between external and internal data to resolve before performing an analysis. Cleansing and formatting data before analysis takes a lot of time, with 80% of the analyst’s time being taken by data pre-processing as reports suggest. Organisations may even need to update information management processes and capabilities to securely store and catalogue external data, because these systems have until now only handles internal data.
Nevertheless, the virtues of external data easily outweigh its faults. For instance, in a pandemic relevant data can be drilled down for use in local areas. The real estate industry makes extensive use of external data to draw insights, such as identifying what suburbs and cities to target based on people’s income levels. Meanwhile, logistics companies use geolocation, weather and traffic data, and data about exceptional events – such as natural disasters – to manage their deliveries and avoid disruptions in the supply chain. Generating such data inhouse is a time-consuming and arduous process that can be exhausting unless you are, or own, a data analysis firm. So external data takes away the pressure of producing relevant data themselves from organisations.

Summing up

As organisations increasingly source data from external sources, they need to take consistent steps to extract the most from this data by enhancing their ability to identify, evaluate, and contract for new data through a data ecosystem. With unrelenting pressure on them to improve the efficiency of their operations, organisations must intensify their pursuit for insights that will help them improve business.

By expanding the universe of data outside of traditional organisational boundaries and adding the dimensions of external data, businesses increase the effectiveness of decision making. Moreover, this shift towards external data driven decision making is putting to use the collective wisdom of crowds that can provide faster, better and cost-efficient predictions that reflect the experience and activity of the many, not the few. By applying external data analytics at the right place, businesses can convert standard decisions to strategic decisions. They can combine this contextualised data with internal data to unlock powerful insights for innovation, growth and profitability.

At Tibil, we believe that while dashboards and visualizations are integral parts of data storytelling, there is much more to it. We work closely with our clients to ask the “why” behind the “what”, and turn your statistics into a powerful communication tool. Tibil has the ability to zero in on hidden layers within your business data, and then materialize this information into clear and simple actions for business strategy.

Get in touch to turn data into knowledge.

Edge Computing: The Key to Smart Manufacturing Success

Edge Computing: The Key to Smart Manufacturing Success

Manufacturing firms world over are in the middle of historic development. With the rise of the IoT, we see a rapid increase in the number of data-centric and interconnected smart factories. Emerging technologies such as automation bring the promise of unimagined possibilities. However, smart and connected devices churn out huge amounts of data at the edge which must be processed almost instantly for Industry 4.0 to reach its full potential unobstructed by data-processing issues.

Edge computing is the concept of moving computing processes as close to the source of data as possible. Instead of relying on distant data centers, it uses local infrastructure to process data. It takes the cloud and brings it to the hardware that’s already all around you. Forecasts suggest that there will be 21.5 billion connected IoT devices worldwide by 2025. Imagine if just half of those could run computing tasks for other devices and services. This vast, interconnected computing network would be particularly valuable for smart manufacturing.

Following are a few benefits that manufacturers can gain by powering smart manufacturing with edge computing:

  • More manageable data analytics
    Big data is the foundation of the new industrial revolution. One of the most substantial advantages of the IoT is how it can improve data analytics. But analyzing all of this data requires a considerable amount of storage, bandwidth and computing power. Edge computing alleviates these concerns in two ways. First, it processes data at or near its source, so the overall process is much faster. Second, each data point within the smart factory processes its own information, thus easing the load off any single system while also refining the process. Since it’s segmented by nature, it’s easier to sift through and find the most relevant information.
  • Expanded interoperability
    An IoT network is only as effective as it is interoperable. Finding compatible devices or systems can be a barrier to the expansion of smart manufacturing. Concerns over interoperability are one of the leading barriers to its adoption, since there’s no standard protocol. Moving computing functions to the edge eliminates some of the need for a universal standard. When devices can convert signals themselves, they’ll be able to work with a greater variety of systems. The edge also serves as a connection point between information and operational technology. It breaks down these distinctions, leading to a more cohesive smart factory
  • Predictive maintenance
    Predictive maintenance means that a manufacturer can use data analytics to pre-emptively detect when a machine will fail and prevent this by conducting maintenance before a potential breakdown. By processing data at the edge, it becomes easier to take pre-emptive steps.
  • Reduced latency
    When a data packet is sent to a data centre across the world, any action that depends on the response can get delayed. For mission critical applications this can be disastrous. In the context of manufacturing, if a connected machine detects a malfunction, any delay in transmitting that data and taking appropriate action can be expensive and can even damage the machinery. Cloud computing can thus be limiting. With edge computing, data can be processed right at the location and the appropriate action can be taken.
  • Better cybersecurity
    While the IoT is great for smart manufacturing, more devices in the network also means potentially more entry points vulnerable to cyberattacks. However, if the processing and storage functions were spread throughout the edge and computing took place closer to the data source, a data breach is much more unlikely.
  • Reduced storage costs
    Smart manufacturing produces a lot of data that needs appropriate storage. Legacy local storage options can be complex and cumbersome, and cloud services can be expensive. Storing data locally helps reduce the data that needs to be stored.
  • Edge AI computing
    ‍Edge AI has several applications in the manufacturing domain, such as enabling the widespread implementation of Industry 4.0 initiatives, including predictive analytics, automated factory floors, reconfigurable production lines and optimized logistics.
    Sensors are mounted on machines and equipment and configured to continually stream data on temperature, vibration and current to the Edge AI platform. Instead of sending all data to the cloud, the AI analyses the data locally and constantly to make predictions for when equipment or a particular machine is about to fail. Manufacturers can process data within milliseconds, giving them real-time information and decision-making capabilities for machine learning intelligence.

Edge Computing – the future of Industry 4.0

Industry 4.0 can only get so far without transitioning to the edge, and will fall short of realising its full abilities In many scenarios. Besides, standard IoT analytics collect data from the edge devices and pass them to the cloud for analysis, then back to the device for action, thus increasing both cost and latency. Training a device to process critical data at the network edge via AI can make manufacturing units much more efficient. Manufacturers are now seeing AI’s potential to move beyond observing and reacting to machine behaviour, to taking a predictive approach, including creating a deeper understanding of signs of failure in operator performance, cycle times, equipment, scheduling maintenance runs and material planning through a 360-degree view of operations.
For edge computing to be able to support manufacturers effectively, the data produced by the myriad of sensors, embedded chips, industrial controllers, connected devices wearable computing devices, robots and drones must be analysed.
After bringing the IoT into the cloud, edge computing is the next logical step. Without adopting this technology, Industry 4.0 will be unable to unleash its full abilities. While the transition to the edge will not and cannot happen overnight, in the end it is all but inevitable.

At Tibil, we believe that while dashboards and visualizations are integral parts of data storytelling, there is much more to it. We work closely with our clients to ask the “why” behind the “what”, and turn your statistics into a powerful communication tool. Tibil has the ability to zero in on hidden layers within your business data, and then materialize this information into clear and simple actions for business strategy.

Get in touch to turn data into knowledge.

Can Your Data Tell a Story?

Can Your Data Tell a Story?

Stop and think for a moment. What do you remember more? The presentation you saw in a meeting last week, or the story you read as a child decades ago? Chances are you’re still more familiar with Jack and his beanstalk than with that sales report. That’s because stories are powerful and visual. Stories inspire, engage, and have the unique ability to transform plain numbers into a compelling narrative and images that excite us. 

So can we apply the principles of storytelling to business data? By presenting data visually in a way that follows a logical path and provides invaluable insights on a particular topic, you can make lasting impressions on your target audience, be it, internal users or customers. Data storytelling, for that’s what this approach is called, is the art of transforming data-driven analyses into an easy-to-consume visual format to influence business decisions and enable actionable insights.

The need for data storytelling

The idea behind data storytelling is the natural human affinity to plotlines and narratives, which makes it easier for us to imbibe complex information simplified in a story format. Easy access to relevant, factual data across industries – warehousing, manufacturing, finance, healthcare, etc – has resulted in our reliance on data for decision making. Unfortunately, data analysts adept in data curation and interpretation, struggle with the task of sharing their insights in an engaging and effective way.
The biggest hurdle between data collection and analysis that prevents organizations from taking data-driven action is the structure of that data. To get the most holistic view, data needs to be pulled from multiple sources – that have multiplied thanks to the advent of digital – which can be very time-consuming and tedious and is made even more complex by different data formats and management systems. When businesses have the right systems in place to provide access to data, and the right resources to analyse and pull learnings from that data, then data can become central to operations and decision-making.

Data storytelling gives business users crucial information about what’s happening in their organization and why, in a manner that is easy to understand and apply, i.e. turn data into action.

How can a data analyst tell a story and not just present cold facts?

Here are some simple ways:

Create context
All successful narratives, fiction or non-fiction, have captivated readers because of their ability to be contextual and connect with the reader on an intimate level. Business data must also be presented in a contextual framework such as trends, market news, background information etc., that helps the useful information pop-out. Even something as simple as the title of the report plays a vital role.

Identify the story
Data presented in any format, be it a presentation or a research report, must begin by asking targeted questions or forming a hypothesis, then bringing together and digging into relevant data to find answers. Some of the questions that can help formulate the story can be goal identification. For example, are you trying to get the buy-in for a proposal? What’s interesting is that often you may end up with a different narrative than the one you started with as you collect and analyse data, and get something that is far more powerful.

Don’t ignore outliers
Outliers are any data that act unusually or outside the norm. Even these data points that do not seem to fit in with the rest of the data you gathered can be very useful, even if it is to further emphasize and support the initial hypothesis.

Maintain a linear timeline
Our brains like a linear format with the basic intro, middle and end. For e.g. data analysts should not start a report with their findings, tempting as it might be. It is better to layout the report by stating the problem statement, followed by the background information and then progressing into the findings.

Create for an audience
Understanding the audience or the typical recipient of the report is crucial so that the data and insights presented are relevant and impactful. Find out what they care about, what their goals are, what they already know, and what additional knowledge will help them achieve their goals.

Formulate a clear narrative
Another aspect that makes a data story different from a regular report is the inclusion of a clear call to action and relevant visuals.

In summary

Businesses the world over are increasingly leaning into analytics to extract actionable insights from the glut of business data surrounding them. Data storytelling helps them communicate key insights compellingly and inspire action that can drive change. However, most of the tables, pie charts, dashboards, and other visualizations fail to resonate with their intended audience and do not offer up any useful insights. At times this happens because data scientists overwhelm their audience with too much data; while other times it happens because the data has been misrepresented or has failed to become an actual narrative that will resonate with recipients.

As a business, if you want your employees and customers to make the right decisions with data, you have to get in their heads in a way they understand. Deriving insights from your data is one thing, being able to present it in an easily digestible way is a whole different ballgame. You need to pay attention to what matters most and have a clear understanding of the format for a great story, you can create convincing narratives through your data and help your audience know better and do better.

At Tibil, we believe that while dashboards and visualizations are integral parts of data storytelling, there is much more to it. We work closely with our clients to ask the “why” behind the “what”, and turn your statistics into a powerful communication tool. Tibil has the ability to zero in on hidden layers within your business data, and then materialize this information into clear and simple actions for business strategy.

Get in touch to turn data into knowledge.

Augmented Data Management and the impact on Advance Analytics

Augmented Data Management and the impact on Advance Analytics

Data has become a vital business asset for all types and sizes of organizations, which are rapidly realizing the fact that data management is pivotal to realizing the business value and unlocking potential. That’s why, over the past decade, businesses have been investing time and money in building a solid data strategy as well as data capabilities such as data governance, metadata management and data quality. With such strategies in place, much higher use of data at an enterprise level is expected. But this increase in the volume of data, its variety and the compelling need to gather as much data as possible, has made data management that much more complex and time-consuming.

Overloaded with the non-strategic tasks of data cleansing and processing, organizations are struggling to stay on top of their data and are finding it hard to scale their data management practices. They find themselves lagging behind in mining their data for insights, in providing adequate user access to users and in maintaining healthy data quality.

Research shows that data scientists spend 80% of their time in low-value tasks such as data collecting, cleansing and organizing, instead of high-value and more strategic activities such as developing data models, refining algorithms, data interpretation, and so on, that are directed at meeting business objectives.

To reduce this everyday hassle and improve data management, businesses are looking to incorporate AI/ML and analytics. Termed Augmented Data Management, this practice involves the application of AI to enhance and automate data management tasks based on sophisticated and specially designed AI models. Data management consequently takes less time, is more accurate and costs less in the long term. According to Gartner, by the end of 2022, we will see a reduction of 45% in manual data management tasks owing to machine learning and automated service-level management.

Let’s look at some of the challenges that we can expect Augmented Data Management to solve and the subsequent benefits.

Data Management Challenges

Large data volumes
Businesses have data pouring in from multiple sources and the amount of this data is getting too big to handle. They are finding it hard to aggregate, curate, and extract value from data.

Poor data quality
Enterprises typically have to work hard to bring the raw data they receive into a validated form fit for consumption. Their task is a tedious process that involves profiling, cleansing, linking and reconciling data with a master source.

Incongruent sources
Enterprise data is mostly obtained from multiple databases and other sources resulting in inconsistencies and inaccuracies. Be it internal or external data, there is no single source of truth.

Data integration is harder
With multiple data elements, huge data volumes, and disparate sources integrating data can be quite challenging no matter how large or experienced is the team of data scientists.

Augmented Data Management to the Rescue

Augmented Data Management, essentially, uses advanced technologies like AI/ML and automation to optimize and improve data management processes for an organization.

Better data
By applying advanced analytics techniques – such as outlier detection, statistical inference, predictive categorization and time series forecasting – instead of only statistical profiling, organizations can attain a higher quality of data, and do so faster than traditional methods. Augmented data management helps enterprises scan all sorts of data and its sources in real-time and churns up data quality scoring with the ability to track, manage and improve quality over time.

Master Data management
There’s a reason why Gartner discussed augmented data management as a strategic planning topic in its 2019 Magic Quadrant for Data Management Solutions. AI and ML models can be used instead of manual, hard-coded practices to match data and identify authoritative sources to verify data and create a single source of truth. ML-driven data discovery and classification will ensure authentic data tagging as soon as it is ingested and will also allow data scientists to perform duplicate data forensics.

Efficient data integration
Traditional statistical methods can be replaced with automation tools that make the process of analysing the data instances faster, simpler and more accurate, especially in the case of hybrid/multi-cloud data management and multi-variate data fabric designs. It also becomes easier to include new data sources and apply algorithms to build real-time data pipelines and bring all the data together for analysis.

Database management solutions
Database-as-a-service solutions enable automatic management of patching updates, advanced data security, data access, automated backups and disaster recovery, and scalability. Users can easily access and use a cloud-based database system without the organisation having to purchase and set up its own hardware or database software, or managing the database in-house.

Metadata management
Metadata management involves searching, classifying, cataloging and labeling or tagging data (both structured and unstructured) based on rules derived from datasets. Augmented data management AI/ML techniques to convert metadata so it can be used in auditing, lineage and reporting. Data scientists can examine large samples of operational data, including actual queries, performance data and schemas, and use metadata to automate data matching, cleansing, and integration, with the assurance that the data lineage is traceable and accessible by users.

Data fabric
We all know that data is available in a variety of formats and is accessed from multiple locations across the world, be it on-premise or in the cloud. Unfortunately, with several applications involved in the process, the data generated becomes increasingly siloed and inaccessible. Creating a data fabric provides enterprises with a single view of all that data via a single environment for accessing, gathering and analysing the data. Data fabric helps eliminate siloes, and improves data ingestion, quality, and governance, without requiring a whole army of tools.

Closing Thoughts – The Future of Augmented Data Management

Perhaps the main advantage of Augmented Data Management is that it allows enterprises to extract actionable insights without requiring too much time or resources. We believe that augmented data management will help streamline the distribution and sharing of data while mitigating the complexities related to extracting actionable insights from that data.

According to experts, augmented data management will support complete or nearly complete automation, where raw data will be fed into an automated pipeline and organisations will get back cleaned up data that can be applied to improve business. Enterprises can focus on strategic tasks that have a direct impact on the business while offering business recommendations. So in the future, we can expect augmented data management to pave the way for enterprise AI data management, thus democratising data access and use across teams and functions.