But don’t get to brooding. Not yet! Instead, it is more advisable to first understand the reasons why most other ML projects end up failing. Once you are aware of the possible and obvious pitfalls, you can simply go back to your project, eliminate them right away, and get your development back on track.

Also, as per VentureBeat, almost 87% of AI projects failed to make it through 2020, owing to a host of intrinsic factors. And as intelligent projects hinging on technologies like Computer Vision and Natural Language Processing cost a lot of money, failing isn’t always an option.

So before we talk about the ways to make your ML project a roaring success, let us delve right into the reasons that enhance fallibility:

Lack of Expertise

Well, the first reason doesn’t need any validation. Machine learning projects require algorithms, data procurement, high-quality annotation, and other complex aspects taken good care of. Having an inexperienced team or data analytics person take care of these intricate ML concepts and developments is waiting for a mishap to happen. Final deployment, continuous monitoring, and successful predictive testing are far more critical and require the assistance of end-to-end service providers.

Subpar Data Volume and Quality

If you evaluate closely, the success of every ML project hinges on Data. Starting from data identification, aggregation, cleansing, augmentation to data labeling, the entire data-specific circle comprises 80% of the total time allocated to developing any machine learning model. 

Image idea: Role of data and how 80% of the time is due to different data-specific tasks

But then, most organizations, in an effort to launch the MVP at a lighting pace, end up compromising on data volume. The lack of diversity makes the models linear and far less intuitive than expected. Data quality also takes a hit if experienced AI training data collection services aren’t requested. 

In the end, it is all about how well Data Science is implemented, and not many ML projects can get this part right.

Erroneous Labeling

Lack of properly annotated data can kill off even the most obvious projects. While labeled data can still be bad data at times, it actually covers the skill sets of your model provided the data collection vendors have done their job to perfection.

Did you know that almost 76% of global organizations end up annotating training data in-house, which stalls some of the more promising AI projects?

Organizations can scale beyond this issue either by outsourcing labeling to experienced service providers or by maintaining adequate training standards for the in-house data analysts, annotators, and scientists.

Lack of Proper Collaboration

Did you ever realize that AI and ML projects can even fail due to intrinsic factors? Surprised, right! Well, don’t be as every organization in charge of developing Machine Learning projects have BI specialists, Data Engineers, Data Scientists, DevOPs, and other professionals working in tandem. And finally, there is the core engineering team that takes the model to production.

However, if one segment fails to interact and collaborate with the other, data quality, volume, algorithms training data sets, testing sets, and other aspects can easily get compromised.

Dated Data Strategy

While a majority of concerns show up in between or at the fag end of the project conclusion, Data Strategy or the lack thereof is an issue that needs to be taken care of right at the onset. The factors included in a data strategy are often as follows:

• Total data requirement
• Can features be extracted from datasets
• What is the mode of data access?
• Does the procured data require cleaning, and if yes, to what end?
• Has the compliance been checked and verified?
• Pairing disparate datasets together for relevance and uniformity

Image idea: Volume of data preferred for organizations

Most organizations planning to launch an ML project follow an obsolete data strategy that only talks about the sources and annotation techniques, which then results in project failure.

Absence of Efficient Leadership

It is important that the crux of the project is defined by an AI leader, who would even understand the practical implications of the product. Having a team of engineers, data scientists, and analysts handling the job is fine but if efficient leadership is missing, the project gets restricted to a mere confluence of technologies and datasets. In the end, it’s all about what difference the project makes and what problem it ends up solving, and these aspects can only be iterated by the business leader itself.

Unanticipated and Unpleasant Data Bias

To be honest, an intelligent ML model is all about automating tasks based on the data that has been fed to it. If the data collection, labeled and trained with is one-dimensional, chances of bias coming into the picture grow significantly. Unsure as to what data bias in AI and ML means? Well, here is an example.

Imagine there is a resume filtering product that picks the best candidate for the job by shortlisting 5 resumes out of 5000. However, the model ends up picking 5 male candidates when there were at least 35 highly qualified female candidates that should have been prioritized. On closer scrutiny, it is identified that the model and subsequent algorithms were fed with male-specific data sets, thereby leading to gender bias and instant elimination of female candidates.

Wrap-Up

While there can be many other reasons for an ML project to fail, these are 7 of the most common yet underrated reasons. And if you look closely, it all comes down to the procurement, usage, deployment, cleansing, annotation, implementation, and transformation of data. Also, as data forms the backbone of any machine learning campaign, it is advisable to onboard a credible end-to-end service provider to handle every aspect of the ML project with precision and accuracy.

The post Why Machine Learning Projects Fail- 7 Reasons that can Take Your Efforts for a Ride? appeared first on Web Updates Daily.

Capture, Process, And Store Information

Big Data is composed of a set of solutions responsible for capturing, processing, and storing large volumes of data. Thanks to both solutions, decision-making is more conscious and secure. On the other hand, Business Intelligence offers tools responsible for providing meaning and direction to the previously collected data.

In developing this process, the adoption of Customer Relationship Management is significant from a commercial point of view. It is an application that allows centralizing in a single Database all the interactions between a company and its customers. These tools will allow you to meet customers from all possible perspectives.

Solutions That Improve Data Management

In the market, there are various solutions to improve the management of company data. Among the existing tools, ERP or “enterprise resource planning” systems stand out. This application allows you to manage the processes of different sections such as finance, manufacturing, accounting and billing, supply chain, human resources, and operations.

On the other hand, we can find Enterprise Asset Management (EAM) software and MRP software. It is a tool for production that plans and manages inventory monitoring.  Any solutions combined with Big Data and Business Intelligence will be very productive and efficient for the company, whatever the sector to be treated. Knowing the customer, their tastes and preferences, their strengths and weaknesses, and the market to which the products are directed will improve its profits.

The post Business Intelligence, The Recipe For A Business To Be Successful appeared first on Web Updates Daily.

When we talk about business intelligence, we refer to using data in a company to facilitate decision-making. It is about covering the operation of the entity, with anticipation of future events. The objective? Have the knowledge to support business decisions.

Therefore, extracting new values ​​and insights from business data is key to delivering actionable intelligence to the company’s entire workforce. There are several points where automation can help an entity to get the most out of its data analysis and business intelligence, as we have outlined below.

Data Quality

The use of erroneous data in analytics and predictive models leads to problems related to loss of confidence and financial impact on the business. The data collected helps identify quality problems before analysis. It is a very time-consuming task. In most companies, professionals spend more hours extracting, preparing, and managing information than analyzing it.

How can automation help in this task? This significantly reduces the time analysts spend preparing and cleaning data. The intervention of professionals in this process is limited to controls and final supervision, dedicating their day to other tasks of more excellent value for the company.

The technology RPA allows any number of repetitive tasks to ensure the quality of the data, to the time that automates advanced processes such as scanning and data collection. Document data extraction and document synchronization are two common ways to automate data management.

Data Analysis From Any System

One of the advantages offered by RPA technology is its integration with other systems that are already in operation in the organization (ERP, in-house design, applications). This enables the scope of business intelligence data and analytical tools to be extended to legacy systems, virtualized environments, and systems without APIs.

Automation can help by extracting and analyzing central financial information and collecting exchange rate data from a website in a format that analysis tools can understand. The combination of RPA with artificial intelligence (AI) goes one step further and enables softbots to ‘manage’ unstructured data such as emails, PDFs, images, handwriting, and scanned documents for analysis.
Unstructured data is compiled into a single document (spreadsheet or database) and is ready for analysis in minutes. This allows companies to drastically reduce the workforce’s hours to these tasks, with the consequent impact on productivity and cost savings for their finances.

Decision Making

The decision to turn them into action is the last phase of the data analysis part, where the professional acts based on the analysis in the BI platform. Detects that there are few units left of a given product. Directly from the program, you can activate a purchase order to restock that item. 

Similarly, an IT systems administrator can start a software robot to review stock and detect incidents without leaving the IT service administration dashboard. These are some use cases, but RPA technology enables other high-impact actions in supply chain management, logistics teams, suppliers, finance, and accounting.

Use Business Intelligence Data In More Complex Automations

Companies are increasingly using data science and analytics to gain insights about their business and make more confident decisions. For example, the finance department can report and act on the credit of invoices that are about to meet payment deadlines. Automating the collection of BI data and then using that data for more complex business processes helps organizations make better decisions faster.

Why Implement Automation In Business Intelligence

What are the advantages for a company to apply automation in the management of BI data? With the implementation of software robots for this task, companies get their professionals to have more time to analyze. Therefore, they can make better decisions, act faster with quality information and avoid making mistakes that take a toll on the—company accounts.

The post Business Intelligence – How Does Automation Help Data Analysis appeared first on Web Updates Daily.

The term Business Intelligence refers to the use of technological tools to analyze the amount of information and data currently handled in any business.

The use of this word encompasses other fields of business analysis such as decision making, strategy design, data management, technical architecture, etc.

What Areas Of Management Does A BI Tool Analyze

In a time of fluctuation of large amounts of data such as the current situation, companies must be more prepared than ever to analyze changes in their environment both internally and externally.

Internally:

• Personal
• Orders management
• Administration
• Accounting
• Communication

Externally:

• Sales
• Orders
• Purchases
• Marketing
• Customers

Why Do We Need A Business Intelligence Tool

Most SMEs and small businesses have some form of formal management structure that allows them to manage tasks daily and accumulate the information they obtain.

However, many of these apart from analyzing accounting and sales in terms of profit and loss do not use their data to predict and plan action strategies.

All the universities and business schools talk about the importance of having a structured and planned vision of the business and the importance of designing strategies and action plans to achieve objectives.

Unfortunately, many companies that started with the intention of analyzing and managing their business along these lines, end up flooded by daily routine and drowned by the amount of data and information that is generated.

These companies are incapable of managing and processing the data and leading, in the worst case, to a loss of vision and control of the business that can have disastrous consequences.

The resistance and adaptability of a company to change is based on the ability to generate profit, no doubt, but also on the ability to analyze and manage a realistic vision oriented to the future.

What Do We Mean When We Talk About Vision

Well, although some believe that it is a desire or the ambitious dream of an entrepreneur, the vision goes far beyond that and must be materialized.

How Do We Do It

Well, shaping it, shaping it visibly.

And that is one of the main difficulties. Many entrepreneurs know they have the information, that the data is there, but they lack useful tools to manage that information and take advantage of it.

Some operate with Excel, databases, and even CRM that allow them to manage and obtain results, but these tools are hardly used and 80% of their utility is wasted.

Why Does This Happen

It could be answered that there is a lack of time or a lack of knowledge, but mainly it is because many of the tools used are not adequate or are difficult to understand.

They lack an optimal user interface and do not visualize the data so that the most important data is exposed to a single screen.

We live in a moment of uncertainty and constant change were the time we dedicate to focus our business on the way out of the crisis must be optimized.

What Are The Most Popular BI Solutions

There is a multitude of software solutions that can help:

• IBM Cognos
• Tableau
• SAP
• Microsoft Power BI
• QlikView
• A3ERP BI
• Oracle BI

The common characteristics of all these tools are that they allow an analysis of the data once it has been processed and present a balanced scorecard for obtaining reports and designing strategies.

All of them are essential applications for the direction and management of the business, since having a vision of all the areas of the company will help them examine the state of their business at all times and support decision-making.

What Is The Best BI Software Solution For My Business

The size, the business area, the number of employees, and above all the amount of data generated to mark the need for one tool or another.

Furthermore, we know that the use of Business Intelligence in a company requires a significant economic investment and an effort in training and adaptation.

However, it is worth it. Getting out of the state of immobility and anxiety generated by the fearsome crisis requires a new attitude, a cold mind, and the security of the knowledge that we have.

It is the moment to make decisions, to have a sure knowledge of our business, to study the behavior of our clients, and to analyze the progress of our sales.

Faced with indecision, fear, and unpredictability, we must have analysis tools that allow us to make the right decisions based on accurate and rigorous information.

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