Big data helps retailers make better decisions. With the right data operation in place, any customer purchase or point-of-contact can become a useful piece of information that determines product strategies and identifies areas of improvement. Purchasing data, in particular, makes it easy to identify items or trends that are already popular with customers, guiding retailers towards offering customers products that they’re already interested in.

Here’s a look at a few retail companies that are using purchasing data to curate their own collections of best-selling products:

1. Coca-Cola

The Coca-Cola Company’s Freestyle soda fountain dispenses beverages in dozens of different flavors: from Diet Coke to sparkling Dasani water. Freestyle machines also let customers customize their flavor combinations, leading to thousands of different possibilities.

Armed with data gathered from the drinks customers were pouring for themselves, Coca-Cola determined new beverage flavors that were already a hit with customers to develop new retail products. One result was Cherry Sprite, a customer favorite that led to the release of its own ready-for-retail canned beverage.

2. Rent the Runway

The popular subscription fashion service Rent the Runway allows users to rent designer clothing on a one-time or recurring basis, giving subscribers access to high-end fashion at a fraction of the traditional retail price. To improve its product selection, Rent the Runway partnered with fashion designers Derek Lam, Prabal Gurung and Jason Wu to power its exclusive Designer Collective, a capsule of outfits created with the help of extensive customer feedback.

Using data gathered from customer surveys about each clothing rental’s fit, the occasion it was used for and the number of times it was worn, Rent the Runway helped the designers determine the types of rentals that were most popular with customers to help guide new designs.

3. Starbucks

Coffee giant Starbucks has embraced purchasing data to inform its entire retail operations. Using exclusive customer data gathered through its Starbucks Rewards loyalty program, the company gains insights into popular drink orders and determines how users are choosing to customize their beverages.

After data made it clear that customers don’t always add cream to iced coffee or add sugar to iced tea, Starbucks developed and released bottled unsweetened iced coffee and K-Cups of unsweetened tea to appeal to existing customer tastes. The frequency and popularity of customer purchases also help Starbucks determine where to build new locations, decide how to optimize its menu boards based on the weather or time of day and boost customer loyalty. 


Inventory management is no longer a matter of guesswork. Thanks to machine learning and prescriptive analytics techniques, better decision-making in operations management makes it easier to predict demand for inventory based on a variety of different factors. With help from all kinds of data, inventory management decisions can be made correctly and successfully to create a competitive edge.

For a retail store, that data can be used to predict and prescribe optimal stock levels — even for products that have no prior sales history. Whether it’s a seasonal product, a new release or an item a store has never carried in the first place, relevant historical information related the product’s details can be used to predict and prescribe its sales potential.

Using big data to assist inventory management can help cut down on costs, maximize sales and ensure a store has the correct amount of stock on hand. The advent of omnichannel retailing and e-commerce offerings like “ship-to-store” options makes accurate inventory management crucial to ensure goods are where they need to be at the right time and aren’t being put to waste.

In one case study, we looked at the distribution and manufacturing arm of a global media conglomerate responsible for shipping an average of 1 billion units of CD, DVD and Blu-ray entertainment titles. Their challenge was to lower operational costs due to declining sales, diminished shelf space and the availability of digital alternatives.

For this vendor, their key goal was to maximize network-wide sell-through of their inventory. Because of the uncertainty of new releases that drive many sales as well as the lack of physical space on an endcap display, determining which titles had the best sales potential was essential.

Harvesting data from sales records, search queries from Google Trends and public sources such as Rotten Tomatoes and IMDb, we built inventory prescriptions taking into account a 150-week period. Building upon each previous week’s data, we scored our prediction performance in order to inform the next week’s prescription. With this data, we were able to make future sales predictions using both data-poor decisions and perfect-foresight decisions.

Being able to accurately manage, predict and organize retail inventory with the help of machine learning leads to retail operations that are far more streamlined and efficient. No matter the product or industry, prescriptive analytics using big data can greatly improve inventory management across the entire supply chain. 

To get an in-depth understanding of the data and methods we used you can download our scientific paper Inventory Management in the Era of Big Data.

At Algo.ai, 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.

As the use of AI enabled platforms continue to grow within all industries and markets, we will also see a greater level of AI platforms being adopted by retail companies. There are four factors that will influence the adoption of AI in Retail:

Think Big, Start Small

Retailers who adopted AI early are already benefitting from this innovation. Retailers that are new to using AI in their day-to-day operations will benefit from starting with the “basics.” It is important for retailers to remember it is not about solving all their problems at once, but to focus on fixing one problem at a time. People often get caught up in the task at hand or distracted with too many problems. It is very important to remember the strategy of “test and learn.” Make one adjustment towards personalization for the consumer and test it before you move on to the next.

AI Boosts Conversions, Revenue, and Customer Satisfaction

IDC Retail insights predicts by 2019 40% of retailers will have developed a CX architecture supported by AI. IDC forecasts customer satisfaction scores to rise by 20%, employee productivity to rise by 15%, and inventory turnover to rise by 25%. This is all going to be possible due to AI paired with AR and IoT data which will give retail companies the ability to hyper-personalize each customer’s experience.

Mobile Devices Will Help AI Flourish

The vast majority of the population has access to mobile devices and conducts most their activities on these devices. This allows for a huge adoption in AI on this platform. The data collected from all these mobile devices will allow companies to improve their customer’s experience. One company that is already successfully implementing an AI platform is Starbucks. One thing their AI platform does is recommend specific orders for customers based on their prior purchase history. AI will play a big role influencing AI adoption in retail.

The Lack of Knowledge and Cultural Biases Will Hold Back the Adoption of AI

Two problems many companies face is the lack of knowledge and their cultural readiness for innovation within the company. These become a problem when people within the company are afraid to innovate new technology they don’t understand. Another hurdle retailers have to jump over is the cost of implementing an AI platform into their existing system.

Download the full report HERE

We’ve all seen the hype around Enterprise Big Data and AI build up over the last few years, culminating in a record year of investments, conferences and implementations in 2017. But how real is AI when it comes to building value for your business today and over the next five years?

Although we are certainly many years away from a human-like AI as we see in the movies; today, narrow or domain-specific AI technologies are already making an impact on bottom lines. Companies that have been smart about adoption and able to quietly implement AI-aided solutions into various functions such as Demand Planning and Inventory Management, Back Office Processes, Sales and Marketing are reaping the benefits.

Because AI can help companies find competitive advantages, demand is increasing at an incredible pace. New companies offering AI enabled software, and other technologies seem to pop up almost daily. Considering the amount of money and brainpower poured into AI research, it won’t be long until commercializing and monetizing data using AI as well as transforming internal processes becomes a necessity to remain competitive.

According to the recently published Teradata report State Of Artifical Intelligence For Enterprises, the majority  “see AI as being able to revolutionize their businesses, automating repetitive processes & tasks and delivering new strategic insights currently not available.”

But with most enterprise software initiatives taking on average 21 months to implement and with Big Data and AI being at the complex end of the spectrum, it is no surprise that 91% see barriers ahead with lack of IT infrastructure (40%) and lack of talent (34%) as the most significant.

So how do you quickly adopt AI successfully across different business functions, driving real and immediate ROI?

AI as a Service

AI Software As a Service (SaaS) adoption is a clear trend that is taking hold in enterprise technology stacks. Adopting SaaS solutions can help companies smooth out their revenues, leading to more resilient and flexible organizations, ultimately allowing a company to deliver better service and products to their clients. With a shortage of talent in this arena and the large data sets required to effectively train artificial intelligence algorithms and implement them into production software, the SaaS model has clear advantages versus trying to develop all capabilities in-house.

Definitive Advantages

The reasons for moving to SaaS offerings can be different for each organization. One of the primary drivers is the potential to create a technology advantage over established competitors and potential disruptors.  Others find they’re increasingly dissatisfied with the way their legacy functions and processes run, and want a better and faster way to see improvements.

Services are defined based on business results and can be expected to produce value quickly, be flexible, implemented quickly, and paid for based on value, business outcomes, or on a seat/consumption basis. This approach leaves more room for pivoting if the ROI is not there as promised, in contrast to traditional capital investment projects where teams often fall prey to the sunk cost fallacy or have a hard time measuring the ROI of their investment.

Enterprises that transition to this model will have a definitive advantage over those that don’t. Companies that don’t shift to aaS models will see their ability to compete diminished, and the same can be said about leveraging AI enabled technologies such as Robotic Process Automation and Automated Insights Generation to name a couple of tangible applications of AI in the enterprise today.

A SaaS tech stack also offers a company greater agility. Traditional industries are consolidating amid increasing mergers and acquisitions, and that means becoming more agile and lean to compete and continue to grow. Service-based models allow companies to trim infrastructure, creating flexibility to scale up or down depending on business needs.

A SaaS model also enables better analytics to derive business insight and help make performance improvements. With clear and contained costs and sometimes built-in analytics capabilities, it is easier than ever to evaluate business results and ROI of investments in services vs. traditional Capex expenditures.

Getting There

Determining how to start adopting AI technologies as well as transitioning to a SaaS and multi-cloud based stack is not necessarily easy. Where to start? With a single problem, department or business need, or do you embark on an enterprise-wide effort?

It can be as simple as starting small with low-hanging fruit and then expanding from there. Is there a department that is last on the priority list for IT but could make some significant gains if given the right tools today? Is there an apparent cost, margin or process that can be identified for measurable improvement? Companies that have seen immediate success often start small and then build on that success. Technology moves too fast these days to allow for extensive planning and execution timelines.

No matter how they get there, in the long run, businesses that transition to service-based models have incomes that are more consistent over time, allowing them to make better and more agile decisions that lead to robustness, flexibility and therefore long-term sustainability.

Building a data science team may seem like a daunting task, especially in this market where talent with practical experience is scarce but interest and buzz in the field is extremely high. Here are a few tips for building and running a successful Data Science team.

Find the Right People

What roles must you fill for a complete data science team? You will need to have a variety of people with different types of skills:

  • Data Scientists who can work with large datasets and understand the theory behind the science. Most importantly they need to be capable of developing predictive models that fit your business context.
  • Data engineers and software developers that understand architecture, infrastructure, and distributed programming.
  • Other roles include a data solutions architect, data platform administrator, full-stack developer, and designer.

Build the Right Processes

The key thing to consider with data science workflows is agility. The team needs the ability to access and work with data in real time. The team then needs to be able to understand business problems and opportunities in the company and implement data solutions that solve those problems or facilitate growth. Make sure they are not handcuffed to slow and tedious processes, as this will limit effectiveness and make it harder to retain top talent.

Finally, the team will need to have a good working relationship with heads of other departments, and clear executive support, so they can work together in agile multi-disciplinary teams to deploy solutions that really benefit the business and will ultimately be adopted by business users.

Choose the Right Platforms

When building a data science competency, it is essential to consider the platform your company is using. A range of options is available from open source to paid services from major cloud providers and innovative startups.

We recommend you maintain some flexibility in your platforms because business and technology moves fast, and you don’t want to tether your team to a tech stack that could become a limitation to their growth and flexibility. Hybrid architectures that utilize the right technologies for the right applications are ideal. Talented architects should be familiar with many different technologies and methods and understand how to select the right components for current and future use cases.

Take Your Time

Most importantly you don’t want to rush and choose the wrong people and platforms or not have quality processes in place. Make sure to take your time to create a team that will work well together, has complementary skills, understands your business, and can deliver successful outcomes that get adopted by the business.

Ensure the Team’s Success

Once you have assembled the right team here are 5 things to keep in mind to maximize the impact they can have as they start building data-driven solutions to give you a competitive advantage:

Discoverability

Data science teams that are not practicing discoverability are writing scripts to solve different functions and not publishing them in a common place. In order for anyone to access this information it usually requires contacting one of the data scientists directly and having them send it over in a presentation or excel sheet. This is both a waste of time for the person asking, and the data scientist that has to devote time to re-delivering rather than innovating. A team that is successfully practicing discoverability publishes their work in a central location where everyone in the organization has access to it.

Automation

The difference between a data science team that does not focus on automation and one that does is quite simple; the team that does not focus on automation is continuously producing results by hand instead of letting their models do the work for them. The team that focuses on automation spends their time maintaining the pipeline instead of manually re-running their workflow. While automation can take more time up-front, it pays off in multiple ways when done successfully. Automated pipelines make it much easier to build the insights and outcomes from your team’s efforts into business processes, continuously increasing the ROI on your data science endeavors.

Collaboration

A data science team that focuses on collaboration and consistency will benefit significantly compared to those that do not. Collaboration allows for the strengths of individuals to help the group as a whole. Collaboration is much easier to achieve when there is consistency between how code is written from individual to individual. Those teams that do not have a shared set of standards will have trouble collaborating and end up with individual quality standards, versioning habits, and coding style. Collaborating with business stakeholders and users is also an important component of successful data science deployments. Great models are useless if no one can use them, users don’t trust them, or they were developed without the correct business context.

Empowerment

Data science teams that agree to use the same stack of tools are better at discoverability and collaboration as well. The trick is to get the right tech stack for the needs of everyone in the team. A team that does not have a cohesive tech stack will suffer from an over-abundance of data storage and analysis tools and a lack of collaborative cohesion. Empowering your teams with tools that make their jobs easier and facilitate the collaboration and automation will set them up for success and aid in job satisfaction.

Deployment

There is a big difference between workflow being “in production” and “produced.” Work that is “in production” means failure is ok and work that is “produced” or finished means failure is not ok. A good data science team will make sure to put tools into production that can be trusted and used to benefit the stakeholders. They will not create things just because they can, instead focusing on the problems that actually need to be solved and making the results digestible and usable by the business.

Data Science as a Service

There are also many options for engaging external expert teams that can accelerate adoption of Data Science while also preparing your organization for growing in-house capabilities.

The same principles apply to service providers and consulting teams. Make sure they are equipped to build continuous value for your organization, not just deliver one-time results.

Sources:

https://mapr.com/blog/how-build-data-science-team/

http://lineardigressions.com/episodes/2017/9/24/disciplined-data-science

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:

https://www.tableau.com/reports/business-intelligence-trends#ai

Analogue: Scalability in Data Usage

At the intersection of big data and machine learning are patterns and analyses that reveal trends and causes. To use healthcare as an example, sensors built into wearable medical devices open windows to improved, individualized healthcare based on a rapidly expanding set of clinical, lab, physiological, and personal data. (A patient diagnosed with hypertension might wear a device that sends information to an application that detects ongoing changes in blood pressure, respiration, or other conditions in real time and alerts a physician when anomalies occur.)

Predictive data technology moves past the goal of gaining insights and into the realm of insights on insights: namely, choosing the trends that require action. If the information received from the wearable monitor is utilized as cross-channel data, the challenge becomes making sense of the insight gained from the data and selecting the appropriate action. With this, the perspective may move from a simple focus on the instantaneous symptoms and treatment of hypertension to a holistic view of the patient’s respiratory, renal, and other systems’ response to standardized treatment.

The Human Factor

The relevance of obtaining cross-channel data from a hypertension patient is most apparent in the universal desire for individualized care. Scalable machine learning searches for efficient algorithms that can work with any amount of data and detect hidden insights. These insights yield logical, adaptive reasoning in performing specific actions, without consuming greater amounts of computing resources. Limits do exist, but predictive data technology adds another dimension to the interpretation of vast data sets. One that, in a business context, means greater efficiency and more thorough self-evaluation on a global scale.0

In the marketplace, insights gained from cross-channel data emphasize the individual’s ability to change. While individuals may defy—with varying levels of deliberateness—predictability, machine learning and predictive data technology take an unrestrained, multi-dimensional view of preferences, real-time behavioral patterns, and possible intent. Thus the view of the “customer journey” is expanded: and a mass of stops at a big box store from which a correlation would have normally been determined in retrospect is now a targeted real-time marketing effort—with the intuition to make progressively better use of progressively expanding data.0

Moving to a New Meaning

Terms like “segmentation analysis” and “adaptive marketing” are themselves harbingers of a system that will soon replace the marketing philosophies of old. However, these new practices may themselves prove to be stepping stones to an even broader view of personalized marketing. Real freedom from scale is measured over time: through predictive data technology that offers personalized strategies for small businesses, large businesses, and corporations as they grow. This new outlook recognizes the consumer’s awareness of the marketplace and the complexity of their decisions, providing insights into profit margins based not only on the instantaneous relationship between product and cost, but also by an adaptive view of long-term customer behavior and loyalty.

Enterprise companies often struggle to ascertain how to optimize all the data they collect. Big data — very large data sets — makes the problem even thornier. However, new data analysis tools such as predictive analytics and analytics as a service enable businesses to quickly harness all the data they collect and turn it into meaningful insights. Self-service analytics is the future of data analysis, letting employees without technical skills manipulate data without writing complex queries.

The introduction of analytics as a service means companies can avoid paying for additional hardware, storage, power and cooling. Utilizing a cloud-based analytics platform also means there is no need to hire expensive consultants and data scientists and allows employees without an IT background to manipulate data.

Big data for better decision-making

Big data analysis, especially predictive analytics, provides businesses with a deeper understanding of customers’ motivations and internal and external factors impacting their business. This knowledge helps companies to foresee what customers will buy, an advantage that can result in greater revenues and reduced costs in comparison with other marketing efforts.

In combination with predictive analytics, prescriptive synthesis enables companies to extract the optimum operational decisions to maximize profits under uncertain conditions.

Predictive analytics delivers significant impact across the customer lifecycle and better results than retrospective analytics (looking at past customer behavior) according to a survey of B2B marketers by Forrester Research.  While 14 percent of surveyed marketers who used retrospective marketing techniques reported revenue growth higher than the industry average, 41 percent of marketers who used predictive techniques experienced revenue growth that surpassed the industry average. Forrester concluded that the use of predictive marketing analytics correlates with better business performance.

Increase revenue and reduce cost

While making better decisions provides a competitive advantage, organizations still have to evaluate whether their investment is saving them money and/or increasing revenues. The average enterprise company spends about $14 million annually on big data projects according to research firm IDG. Calculating the ROI of big data analytics can be difficult. Forbes Insights and consulting firm McKinsey conducted a survey of enterprise businesses for an analytics provider and found that 27 percent of respondents had revenue boosts of three percent or more, while 38 percent had revenue gains between one and three percent attributable to big data analytics.

On the cost side, 21 percent said that they saw a reduction of three percent or more, while 39 percent had cost reductions of one to three percent, In a smaller survey, McKinsey found that big data projects cost 0.6 percent of corporate revenues and returned 1.4 times that level of investment, increasing to 2.0 times over five years.

Maximize ROI

Big data analytics involves a lot of moving parts and variable costs, but with the availability of pay-as-you-go Analytics as a Service it is now much simpler to build and deploy customized analytics platforms with modern interfaces like self-service Visualization and Natural Language features such as chatbots and automated BI reports.

Once the complexity is reduced to a flat monthly fee or scalable usage-based fee, it becomes much easier to identify ROI quickly to understand if an investment is producing the intended ROI. No contracts mean businesses can stay flexible and pivot as needed to support their growth or take advantage of new innovations.

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:

https://www.tableau.com/learn/whitepapers/top-10-business-intelligence-trends-2017