Article | May 27, 2021
The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier.
With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams.
According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028.
With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.
Article | April 30, 2020
In the present complex and volatile market with data as a nucleus, analytics becomes a core function for any enterprise that relies on data-driven insights to understand their customers, trends, and business environments.
In the age of digitization and automation, it is only sensible to make a move to analytics for a data-driven approach for your business. While a host of sources including Digital clicks, social media, POS terminal, and sensors enrich the data quality, data can be collected along various stages of interactions, and initiatives were taken. Customers leave their unique data fingerprint when interacting with the enterprise, which when put through analytics provides actionable insights to make important business decisions.
Table of Contents:
Business Analytics or Business Intelligence: The Difference
Growth Acceleration with Business Analytics
Business Analytics or Business Intelligence (BI): The Difference
Business Intelligence comes within the descriptive phase of analytics. BI is where most enterprises start using an analytics program. BI uses software and services to turn data into actionable intelligence that helps an enterprise to make informed and strategic decisions.
It’s information about the data itself. It’s not trying to do anything beyond telling a story about what the data is saying.
- Beverly Wright, Executive Director, Business Analytics Center, Georgia Tech’s Scheller College of Business
Some businesses might use BI and BA interchangeably, though some believe BI to be the know-how of what has happened, while the analytics or advanced analytics work to anticipate the various future scenarios.
BI uses more structured data from traditional enterprise platforms, such as enterprise resource planning (ERP) or financial software systems, and it delivers views into past financial transactions or other past actions in areas such as operations and the supply chain. Today, experts say BI’s value to organizations is derived from its ability to provide visibility into such areas and business tasks, including contractual reconciliation.
Someone will look at reports from, for example, last year’s sales — that’s BI — but they’ll also get predictions about next year’s sales — that’s business analytics — and then add to that a what-if capability: What would happen if we did X instead of Y.
- CindiHowson, research vice president at Gartner
A subset of business intelligence (BI), business analytics is implemented to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. It is the process of collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. BA is more prescriptive and uses methods that can analyze data, recognize patterns, and develops models that clarify past events, make future predictions, and recommend future discourse.
Analysts use sophisticated data, quantitative analysis, and mathematical models to provide a solution for data-driven issues. To expand their understanding of complex data sets, and artificial intelligence, deep learning, and neural networks to micro-segment available data and identify patterns they can utilize statistics, information systems, computer science, and operations research.
Let’s discuss the 5 ways business analytics can help you accelerate your business growth.
READ MORE: HOW TO OVERCOME CHALLENGES IN ADOPTING DATA ANALYTICS
Growth Acceleration with Business Analytics
1. Expansion planning
Let’s say you’re planning an expansion opening a branch, store, restaurant, or office in a new location and have accumulated a lot of information about your growing customer base, equipment or other asset maintenance, employee payment, and delivery or distribution schedule. What if we told it is possible to get into a much detailed planning process with all that information available? It becomes possible with business analytics. With BA you can find insights in visualizations and dashboards and then research them further with business intelligence and reports. Moreover, you can interact with the results and use the information to create your expansion plan.
2. Finding your audience
You’re right to examine your current customer data but you should also be looking into the customer sentiments towards your brand and who is saying what, and in what parts of the region. Business Analytics offers social media analysis so you can bring together internal and external customer data to create a profile of your customers, both existing and potential. Thus, you have prepared an ideal demographic, which can be used to identify people that are most likely to turn to your products or services. As a result, you have successfully deduced the area that offers the most in terms of expansion and customer potential.
3. Creating your business plan
The real-time interaction with your data provides a detailed map of the current progress as well as your performance. Business Analytics solutions offer performance indicators to find and forecast trends in sales, turnover, and growth. This can be used in the in-depth development of a business plan for the next phase of your thriving franchise.
4. Developing your marketing campaign
With Business Analytics, you’re capable of sending the right message to the audience most eager to try your product/service as part of a marketing campaign. You’re empowered to narrow down branding details, messaging tone and customer preferences, like the right offers that will differentiate you from the other businesses in the area. Using BA, you have gained a competitive edge by making sure you offer something new to your customers and prospects. It enables you to use your data to derive customer insights, make insight-driven decisions, do targeted marketing, and make business development decisions with confidence.
5. Use predictive insights to take action
With analytics tools like predictive analytics, your expansion plans are optimized. It enables you to pinpoint and research about the factors that are influencing your outcomes so that you can be assured of being on the right track. When you can identify and understand your challenges quickly and resolve them faster, you improve the overall business performance resulting in successful expansion and accelerated growth.
READ MORE: WHAT IS THE DIFFERENCE BETWEEN BUSINESS INTELLIGENCE, DATA WAREHOUSING AND DATA ANALYTICS
Article | March 9, 2021
For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions.
Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics.
From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment.
Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings.
Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource.
In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement.
Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data.
The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread.
To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests.
For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials.
In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough.
AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic.
Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests.
Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level.
Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation.
This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer.
This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months.
As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks.
AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation.
AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid.
Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at www.wasrahman.com
Article | April 13, 2020
There’s a lot of information out there related to COVID-19. But right now—when it’s more important than ever to quickly access and analyze data —figuring out how to effectively use COVID-19 data to better manage your business can still be a challenge. We can help. Several customers have leveraged their Incorta platforms to instantaneously integrate COVID-19 data into their enterprise data and analytics dashboards.