At, we’re proud to stand at the forefront of AI research and development. That’s why we’re excited to share some of our research on prescriptive analytics — combining ideas from machine learning and operations research to develop a framework for using data to prescribe optimal decisions.

Descriptive analytics is one of the first steps in data processing: summarizing historical data to produce useful information and prepare data for further analysis. Predictive analytics utilizes statistical techniques such as data mining, predictive modeling and machine learning to analyze historical facts and make future predictions. Prescriptive analytics takes elements of both descriptive analytics and predictive analytics to turn data into optimized predictions.

Operations research and management science typical starts with models and aims to make optimal decisions, leaving the data itself as an afterthought. Conversely, machine learning and data science often begin with data in order to make decisions.

The opportunity that these methods present is to take the significant amount of available data — often big data in electronic form — and develop a theory that unifies operations research and management science that takes data from predictions to prescriptions. The more data we have, the more optimal decisions can be made.

In one real-world example, take the case of a global Fortune 100 media company that sells nearly 2 million different CD, DVD and Blu-ray titles at over 50,000 retailers worldwide. Faced with limited retail shelf space, an endless array of titles and uncertain demand for new releases, how can they best choose which titles to order and in what quantities to maximize the amount of media they sell?

Using a wealth of data and our prescriptive analytics methodology, this company can use prescriptive analytics to fill in gaps throughout the decision-making process to find the best titles to promote. Data was obtained from all available sources, including four years’ worth of sales data spread across their 50,000 retail partners.

Next, data was sourced from online sources such as IMDb and Rotten Tomatoes to determine factors that indicated a movie’s popularity — its actors, Academy Award nominations and box office statistics. Finally, more data could be pared down by location to discern a particular title’s popularity in any given region.

With prescriptive analytics, all data can be considered useful. Even though some data may be more helpful than other data, it can help inform the best solutions and open up previously unseen ways of thinking.

For complete insights into how prescriptive analytics works, download our scientific research paper.

Business intelligence continues to transform the way organizations make informed decisions. Over the past few years, revolutions in artificial intelligence and data collection have made it easier for businesses to gather insights, make predictions and streamline operations.

Tableau, an industry-leading data analytics software company, recently released its 2019 Business Intelligence Trends report to highlight some of tomorrow’s movements in the business intelligence space. Here’s a look at some of the key trends to look forward to:

  • Explainable AI: Artificial intelligence is obviously intelligent, but can it be too intelligent for its own good? Through the coming tend of explainable AI, data scientists will be able to better understand how AI models draw conclusions. This will improve our ability to understand exactly how data is being processed and how processes themselves can be improved.
  • Natural language: Yesterday’s databases require a basic understanding of a query language, requiring users to learn particular syntaxes or make queries in specific ways in order to gain insights from their data. Natural language processing radically simplifies the process, allowing users to ask questions of their data without having to understand the mechanics of doing so.
  • Actionable analytics: Through mobile analytics, dashboard extensions for apps like Salesforce or API integrations, organizations can build analytics capabilities directly into their business operations. The result is a streamlined workflow that enables users to take advantage of analytics tools within their existing applications, taking down silos that tend to interfere with day-to-day work.
  • Ethical data: Because of regulations like GDPR and a greater cultural demand for ethics and privacy in data collection, organizations are beginning to create data with more care and respect. Data collaboratives and ethical data collection processes will continue to focus on using data for the public good and stay within its legal boundaries.
  • Data curationOrganizations have begun recognizing the importance of proper data collection processes. Using data curation tools like data catalogs and semantic governance, a company can better capture, clean and define data so its workforce can take action using accurate information.
  • More clouds: Faster than ever before, data is finding a new home in the cloud. As a result, organizations are rethinking their entire data analytics strategies, as data, applications, and services reside in different locations. Through concepts like data gravity, more companies are choosing to place their applications in the location where most of their workloads reside: the cloud.

Tableau’s full 2019 Business Intelligence Trends report is available here.

Image via McWHINNEY Development

Retailers are encountering new obstacles as big data and the power of the internet progresses. Over the past few decades, consumers have witnessed the ease and efficiency of shopping through the internet.

Organizations have seen this innovation as an opportunity to revolutionize their industry by offering new accessibility options for their customer base. Within the competitive environment of e-commerce sites and online shopping, traditional retailers have to rise to the occasion. To that end, here are five strategies retailers can use to survive in the information age.

1. Revamp the Company’s Website

A new, revitalized website can be attractive to potential and existing customers. An important aspect of revamping the website is increasing the ease of use. Easy-to-use websites generally tend to receive the best feedback from customers.

Companies that shift a large portion of their business to the internet are sometimes called click and mortars. An interesting name change from the original standby of brick and mortar storefronts.

2. Create a Social Media Page

A way to survive in the age of information is to become a part of the age of information. This is possible by creating social media pages for your business. Social media has several amazing benefits. Primarily, social networking sites give businesses a quick, easy, and affordable way to market and reach customers. Also, some social media platforms can track data for you and help you create a targeted marketing plan.

3. Compile a Data Warehouse

A data warehouse lets retailers compile data of their customers and clients. Tracking spending habits, average demographics of customers, and average prices customers are spending are metrics that allows retailers to create an informed business strategy.

Knowing the customer in-and-out is vital to the success of an organization. A data warehouse helps you to compile that metric data and make sense of the information.

4. Partner with Internet Discount Sites

Some companies will partner for a limited time with internet coupon sites like Groupon. These sites will do the marketing for you. In the process, they can help form a new outlet for your retail business to drive sales.

5. Listen to What Customers are Saying

Having an open attitude to change could benefit your retail business. As technology continues to drive innovation, customer feedback will prove essential to staying competitive within the business world.

Ask for reviews and feedback from your customers. Look through the feedback and suggestions from both loyal and disgruntled customers alike. Then, weigh the pros and cons of adopting the suggested changes. Retailers often suffer because they are set in their ways and unwilling to change. Changing is a healthy part of business—and it’s especially necessary in the information age

Overall, the most important way retailers can survive in the age of information is to make that information work to their advantage. So leverage technology to collect metric data and customer feedback. Then, apply what you learn to make worthwhile changes to your processes, products and services. Really, that’s the key to survival.

Now that you’re ready to collect metric data, you might need help analyzing it! Reach out to us at Algo to learn more about how we can empower your company to make informed decisions.

There are many trends coming to the foreground of AI, machine learning, and business intelligence. This article will be talking briefly about some of these trends and why they are coming to light. A link to the in-depth report by Tableau can be found at the bottom of the page.

Do not Fear AI

Is AI the destructive force that will destroy all jobs and the world as we know it? The media and Hollywood have depicted AI as such, however this is not the case at all. At this point in time, machine learning and AI has become a daily tool in business intelligence. These tools are giving time back to their human Analyst counterparts. Analysts are using machine learning and AI software to better understand their company’s data in a more timely fashion.

Liberal Arts Impact on AI

In the upcoming months Liberal Arts will be playing a bigger role in the building of AI and machine learning software. Data scientists are realizing they not only need the data analyzed to be accurate but also tell a story that anyone can understand, including those without a technical background.

NLP (Natural Language Processing) Promise

NLP refers to the way we interact with the AI through the UI (user interface). Companies are beginning to want all level of employees to have access to the data provided by their AI software. The problem many of these companies face is that most of their employees do not have a technical background and no idea how to query a piece of data. This is where NLP comes into play; AI software can process queries in natural language instead of using specific codes. e.g. I want to know the Sales for Item “001”  by day at Store “2045”

Multi-Cloud Capabilities

The move to multi-cloud storage is becoming an ever-increasing desire within big companies. Companies don’t want to be limited to one storage method that may not provide the best performance for their data needs. Though multi-cloud architecture has many benefits, it also has its costs, one of which being the actual overhead cost of running this type of multi-cloud environment.

Rise of the CDO (Chief Data Officer)

With understanding data and analytics becoming a core competency more and more companies are creating a position of CDO. This position allows them to join the C-suite with the CEO, CTO and CIO. This new position gives the CDO the ability to attend the C-level meetings and actually affect change within the company. Due to the creation of the CDO position, companies are showing just how important it is to understand their data and manage it successfully.

Crowdsourcing Governance

Crowdsourcing governance is a fancy term for allowing customers to shape who has access to specific data within a company using self-service analytics. It gets the right information into the right hands while keeping that same information out of the wrong hands.

Data Insurance

Data is more valuable than ever. We have seen countless data breaches over the last few years and will most likely see many more. With customer data becoming so valuable we are going to see a rise in data insurance. This insurance will protect companies from being responsible for a breach of their customer data.

Data Engineering Roles

As data analysis software continues to grow in use and value we will see a rise in data engineering roles over the next several years. Data engineers will begin to transform from more architecture-centric roles to a more user-centric approach within their organizations.

Location of Things

“Location of things” is in connection to IoT (internet of things). We are seeing companies trying to capture location-based data from IoT devices. Gartner, predicts there will be 2.4 billion IoT devices online by 2020. The problem is that companies are trying to collect and compile all this location data within their internal data structures, while most of these structures are not capable of accepting that quantity of data. This is going to lead to great innovations for IoT data storage.

Academics Investments

With data analytics growing in all industries the demand for future data scientists will continue to grow. Due to this high demand for data engineers and data scientists we will begin to see more and more universities offering some sort of academic training in these categories over the next several years.


Read the full report by Tableau Here:

There are many trends coming to the foreground of AI, machine learning, and business intelligence in 2017. This article will be talking briefly about these trends and a link to the in-depth report by Tableau can be found at the bottom of the page.

BI (Business Intelligence) the New Norm

In 2017 we will see a trend of more and more companies using modern business intelligence, allowing analytics to be performed by all employees, not just data scientists and engineers.

Collaboration between Machines and Humans Strengthens

The collaboration and sharing of data is going to move from one-direction, spreadsheets and emails, to an interactive flow of data between multiple parties and their live data stream.

Data Will Become Equal

All data will be equally accessible and understandable. We will be able to access all our data without the worry of it being stored in the same format.

Anyone will be able to Data Prep.

Just as self-service analytics is becoming more accessible to non-technical employees, so will the ability to understand and prep data without the need of a technical background.

Imbedded BI is Allowing Analytics to Grow Everywhere

Business applications like Salesforce are placing analytic tools in the hands of people never before exposed to data. These tools are extending the reach of analytics in our day-to-day lives and we most likely are unaware that we are using them.

Work with Data in a Natural Way

In the next year we will see people being able to access and communicate with their data in a more natural way. We will see this more with the integration of natural language interfaces within AI networks.

Cloud Based Analytics

With data being stored in the cloud we will soon see analytics being conducted there as well. Cloud analytics will be faster and able to scale at a much quicker pace.

Data Literacy will Become a Necessity 

With Data analytics and predictive analysis moving to the mainstream we will see a need for all level of employees needing to be able to read and understand their company’s data.


Read the full report by Tableau Here: