wavelength 150x150 slideshowBusinesses are collecting more data than ever from a wider variety of sources and analyzing it in order to make better business decisions and improve customer service. We asked some executives who are very familiar with BI a few questions about what trends they see in the field and how their own businesses apply it.

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What new trends can we expect to see in BI this year?

Taylor Barstow: Even with the best BI tools, the growing number of data sources and data types means analysts spend 80 percent of their time on data prep. This year, expect to see SMBs and enterprises invest more in automating the processes that feed their BI tools. Those who do will be able to connect their application data sources directly to BI tools faster and more accurately, and accelerate analytics from being reactive to more proactive.

Kevin Kern: One of my favorite scenes in one of the early Star Trek movies has a time-traveling Scotty in the past, comically trying to get information from a computer by speaking to it. Today, we get instant information at home and on the go simply by asking our devices a question or asking them to complete a task. I think that 2018 will see natural language processing become more advanced and more widespread in business intelligence solutions, enabling analysts to vary their questions for specificity, resulting in more pertinent results. NLP will help make BI platforms simple and more intuitive. We are highly focused on cognitive solutions built on an intelligent edge platform that integrate the NLP capabilities of today’s AI with distributed data, and add the cognitive dimension that learns how users absorb and process information to render decisions more quickly. I also think that in 2018, we’ll continue seeing more advanced education in data science programs across the country. As BI continues to advance at such a rapid rate, the demand for graduates who are up to the task and who can even improve upon business intelligence solutions will keep increasing at an exponential rate.

Chip Miceli: Businesses are becoming more risk-aware and will critically evaluate their data center systems – or, if they outsource, the capabilities of the managed services (IT) provider they contract with. Increasingly, the concept of a multi-cloud strategy is gaining traction. Some analysts believe it will be commonplace for nearly 70 percent of enterprises by 2019. Forward-thinking businesses are recognizing that one single solution may not solve all of an organization’s needs.

As the trend toward multi-cloud continues to increase, so too will the use of cybersecurity or cyber liability insurance. This is a hot trend and a needed one. All too often, we see stories of data breaches that impact hundreds, even thousands, of people. And with those breaches come fines and other liabilities that can cripple a business. We only hear about the big cases, but there are many smaller ones too.

Jeremy Paytas: Continued movement toward AI and even more focus on predictive analytics. Data is being collected from many different sources that were previously considered impossible or simply had too much volume. Through technology and data science, barriers are being broken and analysts are now able to investigate/navigate large and (previously) unrelated data. With major data breaches making headlines as of late, there will be even greater scrutiny given to the transmission of data and who has access to it.

Mike Stramaglio: We are already seeing BI in every part of our work and personal life and the impact on us is unbelievable; oftentimes we have become so accustomed to it that it has become a part of our lifestyle DNA.  All we need to do is take a look at Amazon and Uber at a personal level, and as these areas expand the lines become blurred between professional and personal. For example, how we travel, where we travel and the type of travel all become a part of our personal BI profile, and everything we do will be guided and or influenced by our historical patterns of business, leading the way for the history to guide our behavior going forward. We are trending toward no one acquiring cars or other vehicles, but rather paying for cars as a service, where we will be able to pick and choose a variety of cars based on our driving needs that day or month. Same thing with Amazon and Whole Foods, or Uber deliveries, and a long list of other services or business management arenas.  

Give us one good example of BI in action

Paytas: I love that BI has the ability to make organizations more focused and efficient through optimization and predictive analytics. Our company has had great success over the years due in large part to hard work, determination and gut instincts. Through the use of thorough data and analysis we are now able to incorporate predictive analytics and forecasting to identify key opportunities much faster and, most importantly, close deals at a greater rate. There is now more rigor around process, data collection, and input and operations that are paying dividends in reporting and analysis. Because of this, we now have a greater understanding of our customers and can communicate to them in a meaningful manner through their preferred channel(s).

Stramaglio: At the most simple level, Uber is one of the most spectacular examples in that it includes BI patterns that will provide me as a user with everything from visualization and data points regarding ETAs,  types of rides, types of automobiles, routes, etc., and all I have to do is take a look at the data presented and the graphics associated with it,  automating every part of the transportation activity. Of course you can find other examples of BI in action, such as how you heat or cool your home or office or remotely managing other applications because you have been alerted by Ring or Link, and resolving security issues, etc. The list is endless including one of the best examples of BI at work: driverless automobiles. When you think about the BI involved in that scenario you might actually freak out a little bit. 

How has the cloud affected BI?

Barstow: The cloud has brought a set of BI tools that are more user-friendly and easier to use, cutting down some of the time it takes analysts to generate insights using their BI tools of choice. For some companies, it’s also made the results of analytics easier to use and access across the organization. A key step to further accelerate the time to analytics for organizations will be pairing tools alongside BI, which help analysts to more rapidly aggregate, unify and warehouse their source data across all of their source applications.

Kern: The cloud has definitely made BI more efficient, productive and affordable, making sophisticated BI and workflow available to companies of all sizes. BI cloud applications are easily adjusted to accommodate evolution, the number of users and required computer power, even when data volume swells. This flexibility eliminates the need for onsite system upgrades. Not only is the amount of available BI cloud storage virtually immeasurable, the cost of that storage has substantially dropped over the past few years, so a company’s data is available 24 hours a day from virtually anywhere, further enabled through seamless delivery on any platform. 

That said, there are still areas that need to be addressed. For example, complete cloud implementation isn’t always practical for one reason or another, sometimes making it challenging to link necessary data for complete BI reporting. One of the things we’ve recently introduced to clients is a hybrid cloud solution that provides a bridge between data managed in their offices and in the cloud, providing a flexible and secure way of working that combines the best of local and cloud computing.    

Paytas: The cloud has transitioned from buzzword to industry staple. Platforms like AWS and Azure make data more readily available. The sharing and availability of data within companies and the world has never been greater. Leading BI tools and data science platforms are able to connect directly to cloud environments making their use just as easy, or even easier, than standard warehouses. With greater accessibility comes greater scrutiny around data security and tighter restrictions.

What are some of the biggest issues affecting BI today?           

Barstow:  Most businesses have a core suite of SaaS applications where they store data, but the silos between these applications cause problems. Customers have identities across multiple applications, and companies lack a unified view of their customer, thus muddling the results they get from the analytics. The same person appears in multiple applications, maybe even duplicated with an application, without a link between. Some departments might report with different datasets or BI tools. This fragmentation renders crafting solutions to business questions more difficult than it needs to be, and frustrates both executives and data practitioners. The tipping point for the wide adoption and success of BI tools will be automating the processes to get a unified data set available for use by the BI tools and thus accelerating analytics. 

Kern: One of the big issues is getting the word out that BI isn’t only for the big players and that it doesn’t have to be overwhelming or cost prohibitive. Modern BI platforms are being developed and refined every day. They don’t require an exhaustive search for software and high-priced analysts. And these self-service business intelligence platforms are becoming increasingly user-friendly. But even self-service BI won’t properly serve a business unless a solid strategy is in place before deployment, and this, too, is an issue affecting BI today.  Managers too often lack a plan that includes why the data is being collected, what kind of decisions will be made after the data is analyzed, and what kind of problems are being targeted — all of which are exacerbated by the sheer volume and types of data resident in most organizations.  A BI project is only as good as what goes into it, and the lack of a solid strategy too often results in a failed project.  

Miceli: A basic issue is protection and safeguarding of information, which starts at the employee’s computer. There are so many schemes afoot to hack into the infrastructure of individual companies. On the one hand, the ability to capture and optimize the use of intelligence is increasing quickly, but without great education at the basic level (i.e., training employees to understand how to identify threats) – and, of course, developing the appropriate protections further up the line. It sounds like an oversimplification, but education is the biggest friend the company serious about protecting its intelligence can have.

Paytas: Data governance and architecture are two issues that come to mind immediately. Companies have data that exist on many platforms (data warehouse(s), spreadsheets, tools, channels, APIs) and it can cause disconnect within the organization. Architecting the data from the disparate data sources becomes necessary in order to scale and properly utilize the data. It becomes an even greater challenge when individual analysts/users perform independent analysis on incomplete or differing data sources that contradict each other. This can lead to confusion and distrust.

Stramaglio: I honestly believe the biggest issue affecting BI is that technology has not reached a point where it is collaborative enough to collect, store or decipher the various inputs within a common code. It needs to be capable of working in harmony as part of a broader platform, and there are too many points of conflict to provide the most accurate BI today. But it is getting better in leaps and bounds.

What role does visualization play in BI? 

Barstow: The visualization aspects of BI bring BI to life for an organization. BI analysts are dealing with many thousands or millions of data points. The visualization helps distill that down to a simple visual which is usually easier for business users to understand and interpret. As BI tools add more visualization capabilities, it increases the adoption and success of those BI tools. 

Kern: Visualization is nothing new. It’s always been easier and quicker for people to digest information when it is visually presented. Just think of all the signs we encounter in our daily lives. Traffic and road hazard signs are visual. Office signs denoting no smoking, emergency exits and restrooms are visual. Human beings are hardwired for visuals. We process images so much faster than we do text. And with the vast amount of data presented to us in BI, it’s truly critical that we give ourselves the opportunity to absorb data visually. Data visualization also helps us recognize patterns and other things that we wouldn’t necessarily pick up in text. It enables us to quickly identify information and take appropriate action. And visualization allows us to better understand and combine data that might otherwise seem unrelated.   

Miceli: Visualization is key in business intelligence. Statistics show that humans process images 60,000 times faster than text and that these images become part of our longer-term memory. This is critical in understanding data. It’s why lengthy articles come with visuals to break up that “sea of gray” that is the printed or posted word. The proper use of visualization tools can help a client or audience understand the meaning and significance of data.

Paytas: Visualization plays a critical role in BI. To me, the purpose of data visualization is to act as a bridge between the data and its audiences. Once you have a good understanding of your audience, visualizations can really help break down the barriers between data and understanding. By knowing your audience, it is then possible to develop and present the graphs, charts and tables to their preferred method of comprehension. Through data visualizations, our team is able to communicate complex analysis in a manner that can be consumed by various audiences within the organization. Data visualization empowers the end user to make better decisions through the use of trending and benchmarking against other data points to see areas where we’ve had successes or need to focus more resources.  

Stramaglio: Visualization is undoubtedly a critical factor for successful adoption of BI and the corresponding data behind it. Think about Waze and other such high impact technologies that must incorporate visualization as a mandatory requirement for optimal performance. I would also suggest, on the business front, that the adoption of both data/analytics and the best possible dashboards (visualization) is paramount for long-term success. People need to see things now and read them later, which is essential for speedy business decisions and the establishment of reasonable fact patterns.  Visualization/mobility will continue to grow at explosive rates of adoption as the technology ramps up with a much greater need for decision-making urgency. Just look how frustrated people get when an app takes a few seconds to load!

Describe the role you’d like to see for a Chief Data Officer

Miceli: Typically, the Chief Data Officer (CDO) is responsible for utilizing information as an asset – and coming by this information via data processing, analysis, data mining, information trading and other means. The CDO should function as the right hand of the CEO in that he/she has a large role in determining the information that a company will gather, retain and use – and for which purposes. For companies that are in growth mode, a CDO can play a pivotal role in business development in working with new and emerging forms of marketing, including social media of many kinds (geofencing to get very, very specific with data). A CDO should constantly be researching new ways to gather information and how to utilize it for the advancement of the firm — including business development but also for other company operations. In particular, though, marketing — because the business climate is so competitive.

Paytas: The Chief Data Officer would lead initiatives related to data architecture, management and governance and lead the initiatives related to the use of that data through analysis and data science. The CDO would be responsible for bringing data together to make it accessible to the right people to answer questions that enable the business to succeed. It would then be up to the CDO to manage and prioritize the reporting, analysis and advanced analytics (modeling, predictive, etc.) efforts of the company. Another crucial component of the role would be awareness of the opportunities and risks associated with the ever-changing BI landscape when it comes to data, analysis and processes. 

This article originally appeared in the May 2018 issue of Workflow.