The Generative AI Potential: CXO Perspective

A recent global survey done by Freshworks has highlighted that 9 out of 10 (91%) employees are frustrated with their workplace technology. This is despite the pandemic-driven tech spend surge that the world witnessed. According to KPMG, businesses spent approximately $15 billion extra per week on technology to enable remote working during the pandemic. But there still remains a huge gap between employee expectations and employee experience. Globally, the top complaints were slow speeds (51%), slow response from IT teams (34%), lack of collaboration between teams (30%), missing features/capabilities (28%), and lack of automation (25%).

While a fierce war to hire and retain talent is raging globally, CXOs are looking to tap the potential of Generative AI to combat some of the top complaints from their employees. In this blog, we take a look at the potential & impact of Generative AI from the CXO perspective, and the role that it can play in improving business performance as well as employee and customer experience.

 

Economic Potential of Generative AI

Various sectors are incorporating generative AI solutions into their operational workflows. According to an IBM survey, 35% of participants recognized generative AI as a leading emerging technology expected to significantly influence their businesses in the next three to five years.

The economic impact of Generative AI is substantial, as it has the potential to boost productivity, lower expenses, and introduce fresh value propositions. AI functioning as a ‘prediction technology,’ lowers the expense associated with predictions, thereby reshaping business strategies and potentially resulting in the emergence of new avenues for wealth creation. A LTIMindtree report discloses that 75% of businesses in the United States have experienced a minimum of 5% cost reduction through the adoption of Generative AI.

 

Organizational Impacts of Generative AI

Generative AI is already being integrated into many common workplace tools such as email, word processing applications, and meeting software, indicating that this technology is poised to fundamentally revolutionize the way people conduct their work. Here are a few ways that we note Generative AI will impact automation in an organization:

• Natural language will emerge as a new automation language
• More internal processes will be automated and handled by Virtual assistants using generative AI like scheduling meetings and answering common employee queries.
• Generative models will be employed to analyse large datasets and identify patterns or trends in data, providing valuable insights for decision-making.

 

 

Applications of Generative AI

While the applications and possibilities of Generative AI in the workplace are immense, we are still at the nascent stages of Generative AI and the impact of and predictions for automation with generative AI are yet to be seen.

Here are examples from a handful of specific industry sectors to show the potential use cases for Generative AI:

Retail

• Design social media marketing campaigns.
• Create detailed product descriptions.
• Create high-quality videos to demo a product.
• Analyze customer sentiments for hyper-personalized products.

 

Banking and Financial Services

• Help financial institutions make informed decisions on investment strategy.
• Extract information from reports to summarize insights on financial records, customer feedback, account statements, etc.
• Monitor constantly changing compliance regulations and draft compliance statements with supporting evidence.

 

Medical and Pharmaceuticals

• Identify test patient populations, simulate trial outcomes, and optimize clinical trials to accelerate drug development.
• Analyse health records to identify patterns indicative of disease states to support faster, more accurate diagnoses and treatments.
• Suggest treatment options and design a personalized medication plan for patients.

 

Industrial and Manufacturing

• Suggest product design options based on material, cost, functionality, etc.
• Reduce product development timelines with proactive insights into material fitment, design concept development, market research, etc.
• Identify potential issues with a product’s quality through deeper analysis of the data from production lines, sensors, etc.

 

Media and Entertainment

• Create compelling content such as music, artwork, news stories, scripts for TV shows, and even commercials.
• Generate special effects and other visual elements.
• Restore damaged media and imperfections such as scratches from old film footage.

 

Conclusion

As CXOs navigate the complex digital landscape and evaluate the adoption of Generative AI, collaborating with key stakeholders is critical. Working closely with department heads, data scientists, and AI experts, business leaders can ensure a holistic approach to Generative AI solutions being implemented in the workplace. Communication is key and one of the primary responsibilities for senior leaders is to demystify the technology for others. This involves taking a step back to evaluate the strategic implications of Generative AI, understanding the associated risks and opportunities for industries and business models. As leaders craft a compelling narrative for the adoption of Generative AI, they must pinpoint two or three high-impact applications to explore and guide employees through a value-creating journey. This process transitions Gen AI initiatives from pilot tests to rapid scaling, ultimately integrating them into standard business operations. Additionally, senior leaders must commit to developing the necessary roles, skills, and capabilities—both for the present and future—to continuously test and learn with Generative AI and maintain a competitive edge.

Follow us on LinkedIn for more updates.

Companies of all sizes are striving to remain agile and updated, prioritizing their investments in technologies like machine learning, artificial intelligence, and real-time data to improve decision making. This is helping them to accelerate revenue generation efforts, reduce operational expenses and improve protection against risks. Specifically, the impact of machine learning, which is a subset of artificial intelligence, has been revolutionizing various industries and changing the way businesses operate. The ability of machines to learn from data, identify patterns, and make data-driven decisions has unlocked tremendous potential for growth and efficiency across sectors.

Here are a few examples of how various industries are leveraging Machine Learning:

• Healthcare: Diagnosing diseases, predicting outbreaks, and developing treatment plans have all been positively impacted. Machine learning models can also analyze patient data, from medical records to genetic information, enabling healthcare professionals to make more informed decisions.
• Logistics: Industries such as logistics and supply chain management have benefited from machine learning’s ability to optimize operations. Machine learning algorithms can analyze historical data, current conditions, and even real-time factors to predict demand and optimize routes for shipping. This leads to cost reductions and faster delivery times.
• Financial Trading: Algorithms can analyze market data, news, and various other factors to make rapid trading decisions. High-frequency trading firms utilize machine learning to gain a competitive edge in the market. Moreover, machine learning models can detect patterns and trends in financial markets, helping investors make informed investment decisions.
• Agriculture and Precision Farming: Precision farming techniques use machine learning to analyze data from sensors, satellites, and drones to optimize crop management. Farmers can make data-driven decisions about planting, irrigation, and pest control, leading to higher yields and reduced resource wastage.
• Manufacturing: In smart factories, machine learning enables predictive maintenance, quality control, and process optimization. Machines can communicate with each other, detect defects, and adjust processes in real-time, resulting in higher production efficiency and reduced waste. By implementing machine learning in manufacturing, industries can adapt quickly to changing market demands and maintain a competitive edge.

In this blog, we will delve into the profound impact of machine learning on the growth of industries.

How Machine Learning Impacts Growth

Predicting user behaviour:

Machine learning has revolutionized how industries engage with their customers. Machine learning plays a crucial role in predicting user behaviour by leveraging data and algorithms to identify patterns and make accurate predictions. By predicting user behaviour accurately, a company can provide a personalized experience to every customer, leading to not only increased customer satisfaction, but also boosting revenue through higher customer retention and upselling opportunities. Common models for predicting user behaviour include logistic regression, decision trees, random forests, support vector machines, and neural networks.

Spotting trends in real-time:

For any business or industry, staying on top of changes in consumer trends and demands is a critical and ongoing cycle. Machine learning can be a powerful tool for spotting trends in real time by continuously analyzing and identifying patterns, anomalies, and emerging trends in large volumes of data. Real-time trend detection using machine learning enables organizations to respond quickly to changing circumstances, capitalize on emerging opportunities, and mitigate risks. Real-time trend detection is useful for many industries like the financial industry, E-commerce, Healthcare, Beauty & Fashion etc. Algorithms like online learning, time-series analysis, and anomaly detection are commonly used for real-time trend detection.

Enhancing Energy Efficiency:

By harnessing the potential of data analytics and machine learning, the algorithms offer innovative solutions that help optimize energy consumption, reduce waste, and make significant strides towards a sustainable future. Machine learning-driven energy optimization algorithms have the capability to assess data from a range of origins, such as energy meters, sensors, weather predictions, and past usage trends. Through ongoing learning and adjustment, these algorithms can forecast energy requirements and usage, allowing enterprises to cut down on inefficiency, lower carbon emissions, and achieve cost savings.

Improved Operational Efficiency:

Another area where real-time data and machine learning are making a massive impact is improving operational efficiency and plugging wastage. Automating repetitive tasks and streamlining recurring work processes can help to organizations cut down on a lot of wastage and look for better usage of resources. The ability to forecast demand precisely offers further benefits like predicting demand, optimizing shipping routes, restocking as needed, etc.

Machine learning is a transformative force that has fundamentally changed the landscape of various industries. From enhancing efficiency and productivity to improving decision-making, personalization, and customer experience, the impact of machine learning is evident across sectors. As we continue to unlock its potential, industries are poised for growth, innovation, and the ability to meet the evolving needs of a dynamic global market. However, it is important to navigate the ethical and security considerations while ensuring that the benefits of machine learning are harnessed responsibly and sustainably.

Follow us on LinkedIn for more updates.

With the explosion in interest in AI (no small thanks to ChatGPT), businesses the world over are looking at how they can leverage AI to help grow their business. It has become a transformative force in the business world, reshaping the way enterprises operate and make decisions. The best part is this is not just a tech hype, there are actual tangible results in applying AI.  From empowering product, technical, marketing and customer support teams internally to providing key data instantaneously to management and becoming an up-to-date customer interface, the business value of AI-driven processes is immense.

However, the degree to which enterprises invest in and leverage AI varies, as well as the degree of adoption. The type of business also influences the area of AI application. Digital native businesses and those with strong online presence and commerce are interested in customer sentiment, product personalization etc. More traditional businesses are keen on using AI in no-code application development, testing etc. Almost all businesses see value in the customer experience domain. 

As with every other new tech, AI vendors entice the companies with promises of minimal upfront cost, PoC’s, pilots and fantastic outcomes. Many teams are goaded into starting PoC’s, without evaluating the long-term sustained costs and benefits. In this article, we will look at some top AI trends that we in DataLens, a born-in-the-data-cloud company, have had the opportunity to work on.

We will see below some domains which are common across businesses:

 

Top 5 trends in AI in Enterprise

 

Customer Experience

The customer experience starts, rather obviously, with the customer, internal or external.  Digital avatars, voicebots & chatbots are the top AI tools used by businesses. Some of the use cases we have seen include digital avatars representing HR department to serve internal employees, technical support bots that use chatGPT to search product manuals and server customers, etc.  Customer service is an area where a lot of routine, repeatable tasks can be taken over by bots, letting humans take the occasional complex and critical tasks. AI can be used to triage initial contact calls, generate personalized solutions to common problems, and generate reports and summaries of customer interactions.

 

Software engineering

Surprisingly, many customers have ventured into a key IT function – Software (application) development with AI. Particularly for enterprises with large application teams, and custom development projects, using generative AI tools (such as ChatGPT Codex) is a great accelerator. Building coding and other technical skills has been a challenge for companies, and here is where AI in enterprise makes the most difference, and increases RoI. There will be a lot of exciting opportunities for people who have good ideas and a love of solving problems, but not necessarily hard technical skills.

Augmented Applications

From search engines like Bing and Google to productivity tools like Office, social media apps like Facebook, and industry-specific platforms (banking, travel, education), adding AI chatbot functionality is emerging as an effective strategy for driving next-generation customer experience. As AI becomes more adaptive to security, regulatory practices, the use of these add-ons to commonplace applications will vastly increase. For e.g.,  Adobe’s integration of generative AI into its Firefly design tools, trained entirely on proprietary data, to alleviate fears that copyright and ownership could be a problem in the future.

 

Augmented Employee Productivity

There are myriad roles in an Enterprise, many of which are not directly related to the business but play integral support roles. DataLens customers have invested in implementing AI for

• Sales teams outreach to customers (emails, automated calls).
• Process & product documentations and presentations by tech writers
• Routine legal documents

 

AI-Enhanced Cybersecurity

The rise of AI has implications for both cybersecurity threats and defences. On the one hand, cybercriminals are using AI to launch more sophisticated attacks. On the other hand, AI is a potent tool for detecting and mitigating security threats. Machine learning algorithms can identify anomalies in network traffic, potential vulnerabilities, and suspicious behaviour, helping businesses fortify their cybersecurity infrastructure.

 

While these trends showcase the incredible potential of AI in the enterprise, it’s important to note that responsible AI deployment is paramount. Ensuring ethical use of AI, addressing bias in algorithms, and safeguarding data privacy are critical considerations for any business integrating AI into their operations.

 

DataLens’ Dhee platform has modules for Data engineering operations, ML functionality, and Dhee Chat, a LLM-enabled search engine on internal data. Dhee Chat works on cdv, PDF files and Databricks datalake, helping customers securely query their own internal data sources. Dhee is a constantly-evolving platform, built from the DataLens teams’ field experience, and adding features built for customers, constantly. This ‘built-by-engineers-for-engineers’ is the real value that Dhee provides to customers.

Follow us on LinkedIn for more updates on AI in Enterprise.

In a world driven by data, the ability to glean actionable insights from raw information is a competitive advantage. Today, no tech news is complete without a mention of ChatGPT or the power of Generative AI. With everyone (and their neighbor) talking about the positive power of ChatGPT, it obviously followed that others highlighted the risks of bad data, data bias etc. However, we are interested primarily in how all businesses can leverage the power of AI, not just tech and e-commerce companies.

 

The fundamental idea behind the explosion of enterprise interest in ChatGPT is the notion of a real-time, at-your-fingertip insight into the Enterprise’s own and proprietary data. Something that all the powerful BI and reporting tools could not achieve, suddenly seems so full of potential. From a CIO, or a customer support agent, to a product technician, an ability to query your data with simple English, removes the dependency on armies of BI specialists, and pre-defined reports.

 

Understanding ChatGPT’s Potential

ChatGPT, developed by OpenAI, has transcended its initial role as a conversational AI model. Its capabilities have expanded to encompass data analysis, making it a formidable tool for enterprises seeking deeper insights from their own data. This advancement holds the potential to streamline processes, enhance efficiency, and ultimately, boost competitiveness.

Here’s a look at some of its benefits in the context of Enterprise Data:

–        Instantaneous Data Exploration and Analysis

Traditional data analysis often involves complex queries and lengthy processing times. With ChatGPT, this paradigm shifts. The AI can process queries in natural language, allowing users to converse with the system as if they were conversing with a human colleague. This conversational approach enables real-time exploration of data, where users can ask questions, receive immediate responses, and even engage in interactive discussions around insights.

–        Enhanced Decision-Making

ChatGPT’s real-time insights provide decision-makers with a dynamic and intuitive way to interact with their data. Imagine a CTO seeking to optimize resource allocation within an IT department. By conversing with ChatGPT, the CTO can instantly receive information about server usage, performance metrics, and historical trends. This real-time interaction empowers quicker and more informed decisions, leading to improved resource management and operational efficiency.

–        Personalized Data Interpretation

A notable advantage of ChatGPT is its ability to translate complex data into accessible language. This is invaluable for enterprises looking to democratize data insights across various departments and skill levels. Non-technical team members can engage with the AI, asking questions about sales figures, customer behaviors, or market trends, and receive clear, concise responses that facilitate a deeper understanding of the data’s implications.

–        Forecasting and Trend Analysis

ChatGPT’s AI-powered algorithms can also analyze historical data to identify patterns, trends, and potential future outcomes. Enterprises can leverage this feature to make informed predictions and projections, aiding strategic planning and risk management. Whether it’s predicting product demand, anticipating market shifts, or identifying emerging opportunities, ChatGPT’s real-time insights contribute to a proactive and agile business approach.

–        Continuous Learning and Improvement

As enterprises interact with ChatGPT and seek insights, the AI learns from each interaction. This ongoing learning process enables the system to refine its responses over time, becoming increasingly attuned to the specific needs and nuances of the enterprise. This adaptability ensures that ChatGPT’s insights remain relevant and accurate, supporting the enterprise’s evolving data analysis requirements.

 

 

Introducing DheeChat: Your Enterprise Concierge

The Team at DataLens, a born-in-the-data-cloud company, is privileged to have served many large enterprises to build connectors, data validators, automated data pipelines and BI templates. Our AI team has been working with customers to harness the power of LLMs (Large Language Models) in building the data concierge for the Enterprise CIO. A core aspect of this is working with a plethora of LLMs, including proprietary and open source.

 

With prompt engineering being the key domain, our engineers have worked on a simple mandate – How to build the LLM’s around a company’s proprietary data and provide meaningful information to the questioner. The team has utilized the experiences to build a repeatable model, for typical enterprise data repositories.

 

The DheeChat platform

We have incorporated the power of Chat GPT into DataLens’ own proprietary platform Dhee. DheeChat platform leverages ChatGPT engine to provide real-time answers from some typical enterprise data repositories – SQL databases, PDF documents, and the ubiquitous csv files. Building upon our tuned prompt engineering models, DheeChat platform helps a user ask questions and retrieve answers from these common data sources. 

Let’s think about the audience personas here:

– a business-line owner could be asking quarterly sales related info.

– A customer support engineer might want to pull up information from a product manual.

– a data engineer might want to check on a source data file.

 

In each of these cases, we can safely assume that a knowledge of English (or the local business language) is all that is required to search and retrieve the information against the internal data securely.

 

The key features of this platform for an enterprise are:

• Leveraging the power of ChatGPT, securely against internal data stored in the company’s cloud account. Not every business has the resources to build their own LLM’s from scratch.
• The search and retrieval are dynamic – the underlying data could be in a data lake, and constantly changing, which means the insights are real-time. This business value of AI-driven search and retrieval alone is immense.

 

ChatGPT’s role as a real-time insights provider for enterprise data is redefining the data analysis landscape. Its conversational interface, speed, and accessibility make it a powerful tool for decision-makers at all levels of an organization. By embracing ChatGPT, enterprises can unlock the full potential of their data, transforming it into a strategic asset that propels business growth and innovation.

Follow us on LinkedIn for more updates on Dheechat.

With the explosion in interest in AI (no small thanks to ChatGPT), businesses the world over are looking at how they can leverage this technology to help their business grow. The best part is that this is not a technology for the sake of technology, there are myriad ways of readily applying this in a business. From empowering product, marketing and customer support teams internally, to providing key data instantaneously to management and providing updated information directly to customers, the business value of AI-driven search and retrieval alone is immense.

As with every technology of course, the devil is in the details. There is no magic wand AI/ML can wave and produce predictive insights if relevant, clean, sufficient data is not centrally available and ready to be utilized. This is where the legacy data warehouses of yesterday are ceding way to the modern cloud data lakes (Azure, Google, AWS, Databricks, Snowflake etc.). Many of these platforms promise the key benefits like –

• the ability to secure and centralize all data in a cost-effective datalake
• provide access to many stakeholders- data scientists, analysts and so on.

It is but a natural product extension that these same platforms then provide standard machine-learning models, and intuitive (conversational) user-interfaces. As they like to say, ‘The hottest new programming language is English’, meaning that the learning curve to ask questions of the data and get meaningful answers is getting accessible all the way up top, to the business.

So, to begin with, how do we help organizations get data from their multiple sources, in varied formats, to a central cloud data lake platform? From data in their ERP’s (Oracle, SAP), to their modern online-data (Web-clicks, Google Analytics), to modern CRM (Salesforce) data, the format and types are varied and owned by different departments.

The team at DataLens, a born-in-the-data-cloud company, is privileged to have served many large enterprises to build connectors, data validators, automated data pipelines and BI templates. Borne out of such experiences is ‘Dhee’ (intelligence in Sanskrit), a ‘data project accelerator’ to help customers migrate, validate, transform their data to a central platform.

The Dhee Value Proposition

Many of the features of Dhee – Connectors, DQM, Orchestration and Observability – are available in many products and platforms today. So why is Dhee different?

First, Dhee is not a ‘SaaS only’ platform. Customers can install Dhee inside their cloud account and customize it for their requirements. Dhee does not store customer data; it works with AWS, Azure or Databricks datalakes.

The key features of Dhee are

Migration

Dhee is built on open-source, and adds connectors everyday; there are connectors for just about every data source, and if not, one can be built quickly.

Data Quality

Built on top of open-source, Dhee offers an intuitive, low-code interface for data engineers to build their data validation, cleaning and transforming routines. This addresses a key project inhibitor, i.e the time taken to build and use data quality routines.

Multi-Platform Orchestration

Dhee works with AWS, Databricks etc. Jobs built in AWS Glue or Databricks can be orchestrated from inside Dhee. This also allows for enhanced logging across platforms, as we will see in the next point.

Observability

Dhee is built for easy monitoring and control of jobs/pipeline status and captures logs from the AWS/Databricks platforms as well as allowing for data engineers to script detailed logging mechanisms for visibility. Coupled with a graphic interface (Grafana). Dhee’s monitoring capabilities are a stand-out from other platforms that offer similar features.

Above all, Dhee is a constantly-evolving platform , built from the DataLens teams’ field experience, and adding features built for customers, constantly. This ‘built-by-engineers-for-engineers’ is the real value that Dhee provides to customers.

Follow us on LinkedIn for more updates.

In today’s digital age, businesses are sitting on a goldmine of data. By harnessing the power of this data, businesses can gain invaluable insights, make data-driven decisions, and drive growth like never before. However, unlocking the true potential of this data requires advanced analytics and artificial intelligence (AI) capabilities.

We realise that today, data analytics and AI are no longer optional; they are essential for businesses to thrive and succeed. However, for many companies, building a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making. To enable customers to solve this challenge, Datalens has been working closely with Databricks to uncover strategies to leverage the power of data and analytics, optimize operations, and drive growth.

We are excited to announce that Datalens, Databricks and Innopia Global are coming together to organize an event that will help you understand how to aggregate, secure, manage and derive insights from your data to help propel your business forward and revolutionize your operations.

Date: Thursday, 6 July 2023

Venue: Siam Kempinski Hotel

Time: 1.30-5.30pm

Registration Now

Agenda:

1.30-2.00pm: Registration

2.00-2.15pm: Keynote Address by Pallav Jogewar, Director at Datalens

2.15-2.35pm: Databricks Overview by Heather Akuiyibo, VP Sales at Databricks

2.35-3.45pm: Simplifying Data Analytics & AI, Databricks Demo by Yong Hong Goh, Senior Solutions Architect at Databricks

3.45-4.15pm: Refreshment Break

4.15-4.45pm: Customer Success Stories

4.45-5.15pm: Panel Discussion

5.15-5.30pm: Datalens & Databricks Partnership by Pallav Jogewar (Datalens) & Suresh Mylavarapu (Databricks)

Leading industry professionals will be sharing invaluable insights on how embracing data analytics & AI has helped them gain a competitive advantage in the industry. Whether you are just beginning your data journey or have started building a data strategy for your business, this session will surely be an eye-opener to the immense possibilities and potential of data.

This exclusive Data Analytics AI event is Free! There are limited seats available. Register early to avoid disappointment.

Reserve your Seat Here.

How to get to the venue

Venue: Siam Kempinski Hotel (next to the back of Siam Paragon)

Conference venue: Siam Kempinski Hotel (Located behind the Siam Paragon department store)

Public Transportation: BTS Siam Station (Walking to the floor) 2  or the basement of the Siam Paragon department store will have a link between Shopping malls and hotels hosting events ) : Taxi Siam Kempinski

Our Hotel | Siam Kempinski Hotel Bangkok

Siam Kempinski Hotel Bangkok – Google Maps

Follow us on LinkedIn for more updates.

Machine Learning (ML) leverages data to improve performance of certain tasks. ML algorithms use training data to build models that make predictions or decisions without being explicitly programmed to do so.

Machine learning models are beneficial to businesses in many ways, including quickly analyzing vast amounts of data, identifying anomalies, and discovering patterns that would be challenging or time-consuming for a human to perform alone. However, like any other project, investment into a project has to be planned meticulously with clear business value established, in order to be justified. This part is difficult when it comes to Machine learning projects, as each project is unique and no two projects use the exact same workflows. The scop and time variables also vary according to the availability of data, infrastructure, the complexity of the project as well as number of resources required.

There are so many unknown and moving factors, that there is no simple way to estimate the time and cost for ML projects. The only way to move forward is to expect and accept a degree of uncertainty and let your business problem guide you towards the solution.

However, based on our experience working on various ML projects, we’ve developed a 5-step process that seems to work for most clients. With clear objectives and deliverables at each stage, it is possible to estimate the scope, time and cost of machine learning projects. In this article, we’ll share the same with you to give you an understanding of the considerations for a successful ML Project:

Lifecycle Of A Machine Learning Project

The machine learning life cycle is important as it gives a high-level perspective of how the entire project should be structured in order to obtain real, practical business value. The Datalens AI Machine Learning team defines the project estimates based on the following stages:

1. Define Project Objectives: Without having a clear definition of the business problem, it is not possible to find the best way forward. That’s why, we conduct a comprehensive Discovery session which allows us to answer many questions that will determine our next steps. This step usually involves defining the business value, specific tasks & requirements, risks and success criteria.
   Deliverable – A Problem Statement which would define if a project is trivial or or would be complex.
2. Acquire & Prep Data: Data is the primal currency of a Machine Learning project. The next step is to start planning the data requirements and data sets that are needed for the task. What type of data is required? Is the data available in-house or does it need to be acquired? Is the available data in a usable format or does it need to prepared? Depending on the answers to these questions, you could start developing project timelines.Deliverable: Data Pipeline in a format suitable for analysis, most likely into a flat file format such as a .csv.
3. Model Exploration: The next step is to determine the target variable, which is the factor on which you wish to gain deeper understanding. During this phase, the model is trained using the training data and model performance is evaluated which includes model selection, hyperparameter tuning and model fitting the data.Deliverable: A proof-of-concept
4. Model Deployment: Once the best model is determined and the proof-of-concept has been developed, the next stage is where the model is developed and the team works iteratively till they reach a production-ready answer. By now, many of the variables are set and the estimation gets quite precise by this stage.Deliverable: A production-ready ML Solution
5. Test and Improve: Once the model is deployed, the next step is monitor and evolve. This is a continuous phase. Machine Learning projects require time for achieving satisfying outcomes and constant monitoring is required to ensure that there is no degradation of data.

Conclusion

Although the above steps are broadly followed in every project, there are factors that affect the overall cost of ML Projects. Data costs, Research costs, Infrastructure costs and Maintenance costs vary from project to project. Even though the goals might be well-defined, there is no guarantee of whether a model would achieve the desired outcome or not. It is not usually possible to lower the scope and then run the project in a time-boxed setting through a predefined delivery date. However, when you work with a Machine Learning experts like Datalens, they generally know how to foresee and mitigate delays and risks.

Kickstart Your First Machine Learning Project

If you’re considering a machine learning project, why not start today. At Datalens AI, we’ve helped many companies explore machine learning and AI for the first time. We work tirelessly not only to deliver accurate estimates — but to ensure a stellar project experience, backed by:

1. A Professional Development Approach
2. A Dedicated Project Manager + Testers
3. Clean Code That’s Easy To Maintain
4. A Quality Guarantee

 

Follow us on LinkedIn for more updates.

Data is one of the most important resources that enterprises have today. An article published in The Economist in 2017 proclaimed that “The world’s most valuable resource is no longer oil, but data.” Today, the most valuable companies in the world (Apple, Google, Microsoft,
Amazon, Facebook) are all data companies which collect vast amounts of data about their users, analyze this data to generate insights, and use these insights as a resource from which they make money. Other companies are deriving tremendous value from data by using it to
speed up business processes, to avoid errors, to detect and mitigate risks before they occur,and more.

A study by Gartner has revealed that poor quality data is costing organizations on average $14.2 Million annually. Addressing the state of data quality and its business impact is a key priority for chief data officers and data leaders.

Enterprises are rightly placing emphasis on using data intelligently to drive important business decisions. However, having access to datasets is only part of the puzzle. Businesses often struggle with managing the quality of their data, leading to other issues that can considerably harm the company. This is where Data Quality Management (DQM) enters the scene.

What is Data Quality Management?
Data Quality Management is a set of standards to ensure all collected information is reliable, accurate, and meets quality standards. As organizations accumulate huge amounts of data, it is important to ensure good quality of the data – if the input is bad or erroneous, the output will not be accurate either. Therefore, Effective DQM is essential to any consistent data project, as the quality of data is crucial to derive actionable and – more importantly – accurate insights.

Why is Data Quality Important?
Just collecting data is not enough – maintaining the quality of data over time has a lot of benefits for any organization. These benefits include:

• Enables accurate decision-making
• Eliminates duplicate effort and improves operational efficiency
• Ensures Compliance
• Improves customer experience
• Saves cost and effort

The 5 Pillars of Data Quality Management

 

1. Data Cleansing: Data Cleansing is used as an umbrella term for the entire process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. This is a crucial step for maintaining high data quality. There is no one right way of cleaning a dataset. However, many data cleaning techniques can be automated using dedicated software.
2. Data Validation: Once the data is cleaned, data validation is an important step in ensuring the accuracy and quality of data. While it is critical to validate data inputs and values, it is also necessary to validate the data model itself. If the data model is not properly structured or built, you will encounter problems when attempting to use data files in various applications and software.
3. Data Linking: Data linking is the process of collating information from different sources in order to create a more valuable and richer data set. This allows employees to work seamlessly.
4. Data Enrichment: Enriched data is a valuable asset for any organization because it becomes more useful and insightful. It involves combining first party data from internal sources with disparate data from other internal systems or third party data from external sources. Companies conduct data enrichment on their raw data so that they can use it to make informed decisions. In 2018, data enrichment grew by 80% and is growing year on year.
5. Data Deduplication: Removing all redundant information from your data pool ensures that each entity, such as a specific customer or product, is uniquely represented, thus eliminating inconsistencies between different instances of the same data.

As data continues growing in complexity and scale, it is important for business decision makers to prioritize effective DQM strategies for today and for future organizational growth. If businesses can’t process their high-quality data in a way that is both adaptable and scalable, they will not be prepared when another disruptor or volatile market event comes along.

Follow us on LinkedIn for more updates.