Archives

The Impact of Artificial Intelligence on Data Analytics

Posted on Updated on

The Impact of Artificial Intelligence on Data Analytics

By: Brad Starks

We are in the midst of an artificial intelligence boom which is looking to transform many areas of life, business, and technology. According to the Second Annual AI Index Report released in late 2018, which seeks to track AI “volumetric and technical progress on an ongoing basis”, we are in the midst of a sharp uptick in a variety of areas related to AI. Papers being published, startups, VC funding, academics, and overall adoption of AI are all on a sharp rise compared to the 2017 AI index Report (Shoham et al., 2018). Given how large the impact artificial intelligence is poised to have it stands to reason that we will also see AI playing a large part in data analytics from now into the future.

According to SAS, an industry leader in all things data analytics, artificial inelligence can be seen as adding intelligence and value to existing analytics products, particularly analytics (SAS, 2018). Furthermore the growth of AI allows for big data to be better and more efficiently leveraged. We are seeing data gathering taking place at such a rate that it’s no longer possible for humans to process it quickly enough for the data to be of use, as people simply cannot work fast enough on their own. Jeff Rajeck, who is a researcher, trainer, and consultant for Econsultancy, notes that organizations are turning to AI in order to bolster their analytics platforms in such a way that the “systems use machine learning and other AI techniques to help analysts find patterns in customer data, elicit recommendations for optimizing performance, and allow non-professionals to access complicated analytics using simple language” (Rajeck, 2018). Through the integration of AI with analytics people can move away from specifically analyzing data directly, to examining the output from AI and machine learning assisted analytics in order to quickly tell a big picture story based on massive amounts of raw data.

Starting in the third quarter of 2018 Nokia looked to harness the power of artificial intelligence and apply it to their own analytics platform when they released their own AI powered analytics software. Through the integration of AI, the Nokia analytics software will leverage advanced machine learning and deep learning algorithms to provide new levels of prediction and automation capabilities with the ultimate goal of drastically improving the user’s experience with the software through improved performance and output (Nokia, 2018). As a direct result of the integration of AI into the software Nokia expects that users will be able to make predictions six times faster given that the software will be delivering analytics and its own insights much more quickly compared to a more traditional analytics platform that relies on people to do much of the work and analysis of the data.

Given that we are seeing artificial intelligence take over work within analytics that people used to be responsible for, what does that mean for the IS professionals who will be working with the emerging AI integrated analytics platforms? According to ACM inroads, AI is taking over for human interpretation of analytics. This means that “IS professionals need to be ready to contribute to the decisions regarding the boundaries between human and computer-based decision making” as analysts’ job responsibilities shift in the wake of the AI revolution (Topi, 2018). It’s less likely that we will see a reduction in the number of analysts and IS professionals, rather these people will shift from slowly analyzing massive amounts of data to more quickly reacting and interpreting readily available analysis and insights instantly provided by an analytics platform supercharged with AI.

References

Nokia. (2018, March 27). Nokia’s new AI-powered analytics software dramatically improves customer experience and satisfaction. Retrieved December 7, 2018, from https://globenewswire.com/news-release/2018/03/27/1453329/0/en/Nokia-s-new-AI-powered-analytics-software-dramatically-improves-customer-experience-and-satisfaction.html

Rajeck, J. (2018, September 03). The three trends driving marketing analytics in 2018. Retrieved December 07, 2018, from https://econsultancy.com/the-three-trends-driving-marketing-analytics-in-2018/

SAS. (2018). Artificial Intelligence – What it is and why it matters. Retrieved December 7, 2018, from https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html

Topi, H. (2018, September). IS EDUCATION: New IS competency: Integrating analytics and AI capabilities into information systems. ACM Inroads, 9(3), 36-37. Retrieved December 7, 2018, from https://dl.acm.org/citation.cfm?id=3239258

Yoav Shoham, Raymond Perrault, Erik Brynjolfsson, Jack Clark, James Manyika, Juan Carlos Niebles, Terah Lyons, John Etchemendy, Barbara Grosz and Zoe Bauer. (2018, December) “The AI Index 2018 Annual Report”, AI Index Steering Committee, Human-Centered AI Initiative, Stanford University, Stanford, CA. Retrieved December 7, 2018, from http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf

Collaborative Business Intelligence

Posted on Updated on

Collaborative Business Intelligence

By: Adam Pringnitz

Do you ever walk into a meeting to review data with upper management and the information you bring is completely different from what they are seeing?  I know I have done this, and it makes for an awkward discussion, much less getting your feet back under you to move forward confidently.  Thoughts start running through your head…  “Did I get my data wrong?” “Did my boss have the wrong data or interpret it wrong?  I can’t call them out in front of everyone!” 

Research conducted by Shawn Rogers, formerly an EMA Analyst, lays it out the best.  He says “Two heads are better than one.” It may be an old saying, but it is still true today. In today’s fast paced, economically challenged environment, making better decisions is not just a “nice-to-have,” it is vital to the very survival of businesses.”  Every piece of a business needs data in order to make informed decisions.  In a manufacturing environment, Management needs to know what profits are looking like for the year and if they have the additional funds for investment, Sales and Marketing needs to know how conversions are looking and if marketing efforts are paying off, or Production needs to know the amount of orders processed from past years to plan for order increases based off trends to plan for necessary production hours.  Most of this data that is generated, or insights created based off this data is often stored on the user’s personal computer, generally not shared with others except for in a meeting.  What if there was a way to share the insights and data in one central location so Management can see where marketing dollars are being invested and how they are working compared to profits month over month or Sales is able to track an increase in sales demand month to month compared to previous years and compare that to production hours worked? 

Fortunately, there is a way for this to be possible.  Collaborative business intelligence is the merging of business intelligence software with collaboration tools, including social and Web 2.0 technologies, to support improved data-driven decision making.  While this is not a new technology, as the workforce continues to decrease and gaps are not filled from the Baby Boomers to Millennials, the work and collaboration will need to be picked up and completed with less workers.  There will be a larger need for collaboration going into 2019 and further.   According to Monster.com, with 75 million Baby Boomers marching inexorably toward retirement, it’s clear that employers will need more than one workforce plan for replacing exiting workers.  The Bureau of Labor Statistics projects that the labor force rate will continue to drop through 2022.  During the years 2002-2012 the labor force grew by 0.7 percent annually, whereas it’s expected to grow by 0.5 percent from 2012-2022. (Toossi, n.d) 

There are many brands that offer a software for businesses to invest in and have the analytics, data, and insights in one location right at their fingertips.  Brands like Logi Analytics, datapine, and Yellowfin are just a few of the many brands available.  They allow users to gather their data, place into a central location and allow access to anyone they want to use that data.  Each brand offers their own added values and benefits like putting insights into presentations and adding their own comments to that specific graph or create a shareable link to that chart to send out to anyone they feel needs access.

The next time you walk into a meeting be prepared.  Collaborate with other departments often, gain that knowledge of information they might have to share.  Two heads are truly great than one and don’t get yourself into a predicament where you might have to prove your boss wrong.  Get all the facts so everyone is on the same page and set the company up to be prepared to do more with less.  Best of luck on your collaboration of business analytics now and into the future.

Citations

Mitra Toossi, “Labor force projections to 2022: the labor force participation rate continues to fall,” Monthly Labor Review, U.S. Bureau of Labor Statistics, December 2013, https://doi.org/10.21916/mlr.2013.40.

The Rise of Influencer Marketing Within Digital Marketing Strategy

Posted on Updated on

The Rise of Influencer Marketing Within Digital Marketing Strategy

Jennifer Miller

The absolute beginning of Influencer Marketing is debatable.  Brian Mechem wrote that influencer marketing began with the Queen and the Pope, they promoted the use of medicine to those who didn’t believe in it, long ago.  And then officially, Nancy Green became the face of Aunt Jemima in 1890, beginning influencer marketing. The History of Influencer Marketing and How it Has Evolved Over the Years  However, Janna Ehrhardt, for InfluencerDB, offers a different perspective.  She believes this form of marketing began around the 1920s.  It was centered around the idea of using personas to build an emotional response or connection to a product, thus encouraging consumers to purchase the item.  Her examples begin with Santa Claus and Coca-Cola.  Believing that Santa Claus would incite a personal connection, Coca-Cola created advertisements of Santa Claus enjoying the beverage. A Brief History of Influencer Marketing  Coca-Cola began its advertisements with Santa in December 1931, further evolving the marketing to include Santa’s image on bottles, cans, and packaging. It is still being used. Did Coke Create the Red-Suited Santa?  

Determining a definitive date for the conception of Influencer Marketing may not be exact.  However, we know that people influence other people’s decisions.  The more we respect, connect with or admire someone, the more likely we are to be influenced by them to act in a certain way and that includes buying behavior.

Influencer Marketing is not a new way of marketing but is taking hold as a major strategy, especially in the consumer product markets.  Companies are shifting towards influencer Marketing to propel their brand through social media. Marketing departments and hired firms are partnering with social media mavens to promote products across social media outlets.  Some marketing experts predict that Influencer marketing will become the primary way of promotion as social media use develops and grows. 

Photo courtesy of Getty Images

Influencer Marketing can be very effective for brands if the Influencer is legitimate, has appropriate realm of influence in regard to the product/brand, and has the right number of REAL followers.  On all social media platforms, there are ways for Influencers to buy or fake followers to market themselves as a legitimate Influencer.  However, software exists so that marketing departments or firms can research Influencer’s legitimacy.  The new technology that allows verification of the amount and quality of Influencer followers will ensure Influencer Marketing spend is being used effectively. Understanding Influencer Marketing and Why it is So Effective

Photo courtesy of Getty Images

Marketing 2.0 created an opportunity for brands and consumers to engage in ongoing dialogue as opposed to the Marketing 1.0 model where information was a “one-to-many” broadcasted communication.  Marketing has adapted to work with technology, consumer preferences, and therefore staying competitive.  The main idea behind Marketing 2.0 is that social media platforms are now allowing consumers to directly interact with businesses, brands, and Influencers. We live in a semantic web and marketing digital world. Marketing 2.0: Less Spin, More Value

Photo courtesy of Getty Images

Digital advertising can be very difficult because of competition, costs, and getting the strategy right.  One of the key sayings for digital marketers is, “always be testing” and AB (constantly running and comparing the results of different digital marketing content) testing is a big part of the job.  Influencer marketing may be a better advertising channel resulting in higher customer acquisition. The ability to analyze the effectiveness of Influencers for Brands can be done by simple social media monitoring before the promotion begins thus getting rid of the need for digital ad effectiveness (AB) testing. Influencer Marketing: A Stable Acquisition Channel in a Volatile Ads Environment

References

Ehrhardt, J. (2017, October 19). A brief history of influencer marketing. InfluencerDB. Retrieved from https://www.influencerdb.net/blog/brief-history-of-influencer-marketing/

Mathew, J. (2018, July 30). Influencer marketing and why it is so effective. Forbes. Retrieved from https://www.forbes.com/sites/theyec/2018/07/30/understanding-influencer-marketing-and-why-it-is-so-effective/#2dd63ccf71a9

Mechem, B. (2018, March 9). The history of influencer marketing: How it has evolved over the years. Grin. Retrieved from https://www.grin.co/blog/the-history-of-influencer-marketing-how-it-has-evolved-over-the-years

Miachon, N. (2018, March 21). Influencer marketing: A stable acquisition channel in a volatile ads environment. Forbes. Retrieved from https://www.forbes.com/sites/forbescommunicationscouncil/2018/03/21/influencer-marketing-a-stable-acquisition-channel-in-a-volatile-ads-environment/#75115eaf713f

Mooney, P. (2008, January 28). Did Coke create the red-suited Santa? Coca-Cola Company. Retrieved from https://www.coca-colacompany.com/stories/did-coke-create

Sorensen, S. (2010, April 23). Marketing 2.0: Less spin, more value. Forbes. Retrieved from https://www.forbes.com/2010/04/22/twitter-starbucks-comcast-technology-breakthroughs-marketing.html#3f9691283c72