When a power company considers a major new investment, a huge amount of data must be evaluated âŠ and Excel spreadsheets just arenât up to the task. Mercatus offers a better way, by providing a system that houses all this information and allows stakeholders to easily review the assetâs entire portfolio.
When Mercatusâ customers began requesting distributed analytics, Senior Director of Product Management Cathi Grossi and her team saw an opportunity. Creating a data product would enable them not only to deliver greater value and create upsell opportunities, but also to compile anonymous customer data for benchmarking. After briefly considering the âbuild or buyâ decision, they decided to partner with GoodData. (Read the full case study here.)
âBuilding a distributed analytics solution is not the business we want to be in,â says Grossi. âWe didnât have the expertise needed, and there was no reason to reinvent the wheel.â
Thanks to Mercatusâ Pipeline Analytics product, customers can now see exactly where every project is in their pipeline, which helps them make more informed decisions, address issues, and become aware of potential disruptions. The analytics have become an integral part of the selling process, in addition to offering a unique upsell opportunity.
âEveryone wants it,â says Grossi. âWeâre reinforcing the fact that when you buy Mercatus, youâre buying a business intelligence platform for the utilities industry.â
To learn more, read the Mercatus case study, âThe Power of Project Insights.â
So we load up our shiny new visual analytics platform and start slicing and dicing data, creating charts and graphs using the latest designer color schemes, and we might even add a dash of animation. And because we now have so many sources of data, the combinations of dimensions and measures is pretty well endless. As a result we start to see correlations and trends where none had been visible before. These âinsightsâ come thick and fast. Only there are two issues that spoil the party. Are those âinsightsâ really insights, and what do we do with them?
To answer the first question. It has long been documented that human beings invariably add their biases when analyzing data. If we want to find a trend, sure enough we will find one. Whether it is really a trend depends on a deep understanding of the business, and whether that rising line is in any way justified. If not, then it is probably nothing more than noise dressed up to look pretty. So we have to be very careful when digging around in data. We should understand that correlation is not causation, and that many of the âinsightsâ we gain might be nothing more than randomness made to look respectable.
The second question â what do we do with the insights â is complicated. Letâs examine what might need to happen to make an insight actionable:
- Business managers must be convinced that the insight is real and can be trusted. It becomes necessary to create a story, presenting all the evidence, with explanations of why the various charts and graphs are the way they are. This might seem like it should be a straightforward process, but letâs remember that change usually involves treading on someoneâs toes. So there will be resistance from those who will need to implement change, and detailed scrutiny of what is being presented, with ruthless exploitation of any holes in the evidence being presented. Think carefully before you poke a stick in the hornetâs nest.
- If there is broad agreement that the analysis is correct, the next step is to decide how business processes need to change. The change might be trivial, or it might go to the heart of several business processes. If it is the latter then it may take months to redesign processes and get agreement from all involved.
- Once implemented, the changes need to be monitored, and so new reporting and analysis procedures need to be put in place. Fine tuning might need to be made, or heavens forbid, we might find that matters have not improved in the way we expected. New analysis, with new âinsightsâ might suggest additional changes. It might be best to keep quiet about that one.
It should be clear from this that analysis is really just a small part of the insight-to-action journey. While suppliers of analytics tools flatter business users that their insights are important, the reality is somewhat different. Most insights will not be insights at all, but random noise making itself look respectable. Those insights that have been interrogated and found to be true mean nothing unless business processes can be changed, and this is where the real hard work is to be found.
Before we obsess over charts and graphs we should ensure that the results of analysis can be implemented in a controlled, documented, measurable manner. To this end standards such as Decision Model and Notation are being introduced, as a way to integrate decision logic into business processes. After all, making the results of analysis actionable generally means that people make different operational decisions.
Right now we are fascinated with the shiny new things â just because they are new and visually attractive. The real work consists of having people who know when randomness is trying to pull its tricks, so that analysis actually means something, and having the processes in place to transform significant insights into action. Anything else is just a waste of time.
Within half a decade we will have AI powered BI, and so the task of finding meaningful insights in our data will become easier. We will also have the methods and infrastructure to quickly move from meaningful analysis to modified business processes. Until then we need to be cautious and expect that actionable analytics will become more of a reality as the whole process becomes automated.
According to a recent article in EconoTimes, a new report by Zion Research shows that the data-driven global business intelligence (BI) market accounted for USD 16.33 billion in 2015 â and that it could reach USD 26.50 billion by the year 2021.
âBusiness intelligence helps companies make better decisions,â the article states. â[S]oftware will improve the visibility of processes and make it possible to identify any areas that need development.â
The Zion report goes on to identify the largest market for BI growth as North America, which accounted for 86% of the global market share in 2015, and lists GoodData among the major industry participants in this worldwide trend.
Here at GoodData, we were very interested by this report for obvious reasons, but we were also thrilled to see that several of our key strategies are being validated by market trends. We believe that true Business Intelligence is about more than dashboards; itâs about making actionable, data-driven recommendations that inform actions, and making sure those recommendations are embedded seamlessly into workflows to maximize their effectiveness.
The report supports this strategy, noting that BI is no longer tied to desktop computers: mobile business intelligence (MBI) now accounts for more than 20 percent of the global business intelligence market. Not only do mobile BI apps facilitate the viewing of data for an increasingly mobile workforce, but they also allow data captured via mobile devices to be incorporated instantly so that reports can be updated in real time.
Zionâs research also found that while on-premise analytics still account for 86% of the market share, this number âis projected to witness a decline in its shares by the end of the forecast period.â As a cloud-based analytics company, we have long expounded on the benefits of moving BI and data warehousing environments to the cloud. Among other benefits doing so speeds deployments, avoids capital expenditures on hardware infrastructure, simplifies software upgrades, and minimizes the need for IT involvement, and weâre very happy to see the increased speed of adoption for cloud solutions in the BI marketplace.
For more details, you can read the full article here.
To learn more about how to define and measure your dataâs usefulness and monetary value, download the Eckerson Group report on Data Monetization Networks.
As we enter the holiday season and people start talking about their favorite time of the year, I also like to reflect on my favorite work time of the year - GoodDataâs biannual Customer Advisory Board. Itâs so energizing to get a small group of thought leaders and visionaries into a room for 2 days to show off what theyâve built, talk about their plans for the future and discuss how in partnering together we can see those ideas to fruition.
Iâve overseen Customer Advisory Boards for the past 15 years with various companies Iâve worked with, and theyâve always been a highlight. This yearâs CAB was no different, so without further ado and here are some of the key lessons Iâve learned.
- The product team owns the content. Everyone, and I mean everyone, wants to attend a CAB and have a speaking slot because itâs not often that multiple customers are in one room for 2 days at the same time. And really important information is shared. AND itâs also a chance to meet customers face to face. AND itâs a LOT of fun. But, other groups have a regular cadence of discussion with customers and this is the product teamâs opportunity to hone in on the product roadmap. Customerâs want to share their visions and goals and influence how the product can help them meet those goals. I canât tell you how many times weâve shared a roadmap only to learn that the one âbigâ thing we thought customers wanted was a lower priority than we thought. Prioritization exercises are great learning experiences.
- Have specific goals and agenda that will help you meet those goals. Itâs easy to fall into the trap of wanting to get every question answered and show off what youâre building and planning. In order to have an effective CAB you need to focus. After the CAB youâll have plenty of opportunities to continue the conversations now that a relationship has been established.
- Keep it small. The urge to invite every âimportantâ or âbigâ customer to join is one that needs to be curtailed. Every customer is important but that doesnât mean that all customers are comfortable sharing in groups, or will have interest in the particular product features you will be discussing. Smaller allows for more in depth discussion and participation. There is nothing worse than having members that donât participate. You donât want to waste anyoneâs time.
- Invite customers based on your goals - not based on name, size or previous participation. This goes back to number 3. Every CAB has a different focus or goal and that might mean that some people who have participated in the past wonât for this particular meeting. That doesnât mean they arenât important anymore, it means that their use case isnât one you will be discussing this time and it wouldnât be a good use of their time. Itâs hard to not see the same faces as youâve built a rapport but hopefully you are still engaging them in feedback.
- Allow enough time for networking and sharing of ideas. It always surprises me how much time our customers want to spend seeing what others have built and sharing ideas among each other.
- Donât talk - LISTEN. This is the HARDEST lesson of all but the most important. You are a facilitator of conversation and sharing of ideas. Customers do not want to be talked at - they could listen in to a webinar if thatâs what they wanted. This is their opportunity to share their ideas with you. Itâs important that you hear them and not feel the need to always respond. Iâd also encourage you to consider inviting customers who may not currently provide a glowing reference. Some of the best ideas come from brainstorming together with a less than happy customer. And what better way to get them re-invigorated about the relationship than to have worked together to find solutions to their visions?
Frankly, I could go on and on about the benefits and joys of hosting Customer Advisory Boards. So Iâll end with a great big thank you to our current and past CAB members. Without them, we wouldnât have built the amazing company that we have.
For hospitality managers, cost control is a constant juggling act, involving inventory management, personnel, pricing, and a host of other factors. They have the data they need to make informed decisions; they just donât have the time or resources to analyze and distribute it effectively, and most analytics packages are beyond their budgets.
Enter Fourth, the worldâs leading provider of cloud-based cost control solutions to the hospitality industry. The company saw an opportunity to offer an analytics platform alongside its existing purchase-to-pay, inventory management, and other solutions.
âOur mission was to create a solution that everyone could afford and could take advantage of,â says Mike Shipley, who coordinated Fourthâs project with GoodData. âWe wanted to empower our customers to run their businesses better â to make the information easy to access, and to offer it in a graphic, interactive interface that they can use everywhere.â (Read the full case study here)
In 2013, Fourth and GoodData partnered to design and launch a data product that was unlike anything else in the hospitality marketplace. The new platform allows customers not only to improve business performance by enabling decisions based on actual data, but also to automate distribution of the right data to the right stakeholders in an easy-to-read format.
One Fourth Analytics customer, GLH Hotels, was able to identify an opportunity to increase its margins simply by changing its staffing profile. âNow we have a greater percentage of employees on part time and flexible contracts, with the obvious effects on the bottom line,â says GLHâs HR Programme Manager Andrew Elvin.
Since Fourth Analytics went live, the product has delivered â and continues to deliver â a host of benefits to the organization, including
- New revenue opportunities
- Added value to current customers
- Faster deployments
- Thought leadership
- ROI of 117 percent
To learn more, read How Fourth Added Analytics to Its MenuâŠand Realized a 117% ROI.
Over the next few years, the market for data-driven products and services is expected to grow by 26.4 percent â six times the rate of the tech sector as a whole â according to IDC Research.
Driving this growth is the realization that data products can create not only a new source of revenue, but also a unique competitive advantage. However, many companies become stuck on the âhowâ â how to create the product and launch with an effective go-to-market strategy.
This âhowâ is the subject of an article I recently contributed to Data Informed, â4 Steps to Designing and Launching an Effective Data Product.â If its data product is to succeed, the organization must Determine goals and develop a user-led product design
- Establish pricing and packaging
- Roll out a successful launch
- Continue to add new features and functions
For a detailed description of each step â and a real world case study â I invite you to read the full article here.
To learn more about choosing the right embedded analytics solution for your organization, download the report Which Embedded Analytics Product is Right for You?
The results of this weekâs presidential election came as a shock to many people. But as I sat glued to CNN, watching as more and more states flipped from Blue to Red, all I could think of was how this election stands as a stark reminder of the risks that come with predictive analysis technology.
Politics, just like every other industry, has become enamored with the power of big data. And as political analysts crunched the numbers and ran predictive data models over the past months a Hillary Clinton victory seemed almost assured, with the major vote forecasters placing her chances of winning between 70 and 99 percent. Of course we all now know how wrong those predictions were.
The promise of predictive analytics is an intoxicating one, and itâs easy to see why. Traditional analytics technology is primarily retrospective, but radical advances in AI and computing power have promised to shift this paradigm to a forward looking one. Using machine learning and predictive analysis, itâs now possible to come up with reasonably accurate predictions about what will happen in the future. But this new wave of technology comes with heightened stakes and greater risks, and the near total failure by political experts to foresee Donald Trumpâs victory perfectly illustrates the pitfalls of flawed assumptions, wide margins of error and lack of context when it comes to predictive analysis.
The ability to use technology to gaze into huge amounts of data to discern upcoming trends and inform future actions can be a massive advantage, and I do believe that AI will transform the business landscape especially as far as business intelligence and predictive analytics are concerned. But this technology is young and still developing, and predictive algorithms are not magic. They rely on data models that can be based on flawed assumptions, and if those models are wrong the results can be disastrous. Just look at the event known as Knightmare, where the Knight Capital Group lost $440 million in 30 minutes when their trading algorithm went haywire.
My point is that predictive analytics, while potentially game-changing, have limitations. And as professionals from every industry become ever more reliant on these tools to make increasingly important decisions, itâs more essential than ever to appreciate and account for these limitations. Predictive technology gives probabilities, not guarantees. That is why there will always be a need for experienced, insightful experts to plug the gaps left by this type of technology. Only by applying years of experience and expertise can one take those probabilities and apply the intuition, creative thinking and context needed to make truly informed decisions. As Steve Lohr wrote in a recent New York Times article, âdata is the fuel, and algorithms borrowed from the tool kit of artificial intelligence, notably machine learning, are the engine.â
But we still need to be in the driverâs seat, remembering to never take our eyes off the road.
Last month I had the opportunity to attend the Fast Casual Executive Summit in Orange County, where I led an interactive âBrain Exchangeâ session about âAnalytics as Assets.â This discussion was all about how QSRs are leveraging analytics to increase their trust with operators and automate key workflows, allowing them to dramatically improve margins. After the event, I sat down with Shelly Whitehead from Fast Casual for a Q&A session about some of the insights that were shared. Here are a few of the highlights:
- Analytics arenât just beneficial for large brands. With respect to sharing data and analytics, some of the participants from larger organizations were surprised learn that the smaller brands were just as passionate and dedicated to innovation as they were.
- Correlation is key. Simply measuring and communicating metrics is not enough; there is an urgent need to understand why metrics move. By understanding cause and effect, restaurateurs can take better informed actions and create opportunities to innovate (e.g. If restaurateurs understand how a social media marketing campaign impacts demand and traffic, they can leverage data to drive smart menus and automate scheduling.)
- Think about your servers as end users. Most of the time, we think of the end user in terms of management, suppliers, partners, and those in HQ. But our discussion revealed lots of opportunities to deliver information to servers to help them optimize and maximize their individual tickets, which not only helps them individually but also drives success for the restaurant itself.
These are just a few of the insights from our full discussion. Restaurants today face serious challenges, both internally in the form of large increases in operating costs and externally from emerging business models from companies like Blue Apron, Munchery and others. However there are also massive opportunities for innovation in the restaurant sector, and the leaders I spoke with all commented on the value, business impact, and benefits of distributing analytics can have on their organizations.
To learn more about the possibilities of distributed analytics for restaurants, download How Big Data is Revolutionizing the Restaurant Industry:
It seems that lately, I canât login to Twitter or open LinkedIn without being bombarded by articles about the coming wave of transformative business opportunities that will be created by of the Internet of Things. They herald IoT as the new frontier of economic growth, with Cisco estimating that connected products have the potential to generate more than $19 trillion of value in the coming years. But even as the promise of IoT has ensnared the minds of the business community, many have ignored or downplayed the massive elephant hiding behind the mountain of gold: that actually monetizing IoT is going to be hard. So hard, in fact, that Gartner estimates that through 2018, 80% of IoT implementations will squander their transformational opportunities.
Iâll give you an example: telematics have the potential to completely transform the insurance industry. But right now, most companies are focused on the data plumbing, rather than putting that data to work making business impacts. In short, the vast majority of the data being generated by connected devices remains undervalued and unmonetized.
There are exceptions. Automakers are leading the way in consumer facing IoT monetization, and in the B2B space Nest is a powerhouse with their Learning Thermostat product which enables them to offer energy management services to utilities. But as Gartner notes, the trend towards treating and deploying data as a monetizable asset is still in the âearly adoptionâ phase.
I believe this shortfall is largely attributable to the fact that Iâve seen very few business leaders that have laid out concrete plans to turn the data being generated by our âthingsâ into actual revenue. Capgemini Consulting notes that although 96% of senior business leaders have said their companies would be leveraging IoT in some way within the next three years, over 70% of organizations do not generate service revenues from their IoT solutions.
This is a huge issue, as to remain competitive and thrive in the digitally connected economy enterprises will need to find ways to monetize IoT data and harness it to enhance performance and create new business value. The problem is that most organizations are poorly equipped when it comes to the knowledge or experience needed to capitalize on monetizing their information assets. But as Albert Einstein famously said, âIn the middle of difficulty lies opportunityâ; and monetizing data from IoT can be a major competitive advantage for the truly innovative organizations that are doing it successfully.
A key piece of solving the IoT data monetization puzzle will be choosing the right technologies and partners to help them achieve their goals. The flood of data from the IoT will be wasted unless organizations can analyze and package it in a valuable way, and Gartner states that most opportunities for monetizing IoT data will involve some level of refinement and analytics. I believe that embedded Smart Business Applications that leverage artificial intelligence will be an integral part of the solution, as they provide the vital layer of âconnective tissueâ that can turn the raw data generated by IoT devices into actionable insights that drive value.
AI will transform the business side of IoT, with applications for every industry from automotive to healthcare and insurance. IBM Watson, Google Now, Alexa, Siri and other platforms have opened peopleâs eyes to the possibilities of AI, and business users want that technology applied to the tools that make them better at their jobs. Truly âsmartâ business applications will harness the power of AI by taking data from a myriad of connected sources and applying predictive and statistical analysis to that data to ultimately predict outcomes and deliver powerful insights that recommend and inform actions to business users, unlocking monetization possibilities.
I never imagined that a simple Tweet could lead to me sitting across a table from one of the most influential thought leaders in my field, but thatâs exactly what happened to me earlier this year when I met Nir Eyal.
I gave a Data Talk video about designing and launching data products back in April, where I mentioned that GoodData follows a data product model inspired by Nirâs groundbreaking book Hooked. Nir is well known as a premier expert on creating âstickyâ technology products that users employ over and over again out of sheer habit.
When the video and the accompanying blog post were published, I shared them on Twitter, with a nod to Nir for his inspiration. Nir actually responded, and those tweets led to a meeting over lunch.
Toting a suitcase full of books for him to sign for our customers, I met Nir for lunch. After clarifying that I was not, as heâd mistakenly assumed, the founder of GoodData, I posed a question that had been on my mind since I first read his book: How do you make technology âstickyâ â or to use his terminology, âhabit-formingâ â if itâs not something people use on a daily basis?
In Nirâs book, his examples of sticky products include the iPhone, Twitter, Facebook, and Pinterest â all products that people are likely to use every day. But the data products that we create are different, more likely to be used on a weekly or monthly basis. So whatâre the best strategies to make these products habit-forming?Make It Personal
The first thing we can do, Nir shared, is make sure that our data products surface the insights that matter most to the end user. In other words, we need to make it as easy and intuitive as possible for users to find and absorb the analytics that are most pertinent to achieving their goals.Go With the (Work)flow
The second insight Nir shared with me is the importance of embedding data products into usersâ regular workflow. Instead of asking them to click over to another tool or another online portal, we make those analytics an integral part of the tools they use on a regular basis, increasing the likelihood that they use them.
I walked away from our lunch meeting with much more than a suitcase full of signed books (thanks, Nir!). I had confirmation from an objective industry expert that weâre delivering what our clients need in a habit-forming data solution. By delivering products that allow personalization of reports and embedding of solutions into regular workflows, we empower our clients to offer value-driven products that arenât simply useful â theyâre habit forming.
For more insights on designing and launching data products, keep an eye out for my upcoming article on the Data Informed website.
A couple of weeks ago, I attended the Big Data, Big Business event organised by the Universal Startup Centre at the Node5 accelerator in Prague. While I was there I had the opportunity not only to talk about my thoughts on the future of big data, but also share some of my experiences as a founder and business leader.
The thing about leadership is that nobody will tell you how to do it; ultimately, you have to figure it out yourself. But after more than 20 years in the game, Iâve learned some key lessons, which Iâve listed below.1) Find Your âProduct/Market Fitâ
The most important task of any founder is to find harmony between their product and the marketsâ demands. But 99 percent of companies cease to exist because they fail to correctly find their market fit, and align their products to it. Entrepreneurs struggling with this should read Peter Drucker, who has some amazing books on the subject.2) Set Up Goals and Get Out of the Way
The enthusiasm of a group of smart people working together to achieve a certain goal is what makes a great company. Founders of successful companies should set goals, but shouldnât dictate how to reach them. They should give feedback where needed, but always allow the talented people that theyâve hired the freedom and trust they deserve to get the job done.3) See to Company Values
Ten years ago, no one cared about company values; today, they are often what attract people to a company. Strive to be your best self, and have your company strive for the same. Just remember, the company value of a small or big company is defined as the worst behaviour of your managers you are willing to tolerate.4) Have a Mission
When I ask my employees what our mission is and they donât know (or I watch them struggle to think something up on the spot) thatâs bad. Each businessperson should know where they are heading. Each company must have a clear mission to know what it is doing. And each employee should know that mission, and if they donât know the mission teach them until they do.5) Communicate
Having a vision and mission doesnât mean anything unless youâre able to communicate it to three key entities: your team, investors and customers.
Take investors, for example. You have to be able to persuade them that if they donât invest in you immediately, theyâll lose money. Youâll know youâve nailed this communication skill if investors start calling you back soon after you meet with them; if they donât, theyâre simply not interested.6) Focus on Your Customers
Too often, I see technology companies over-concentrate on their products or on their founder. It is absolutely critical that founders focus on understanding what, why, when and how customers buy, as during a businessâ startup phase it is usually the founder who brings a new product to the market. So my advice is to serve your first 50 or 100 customers yourself, to really get to know their needs and what your business can do for them.7) Know How to Say Yes/No
Effective leadership is like playing sports. You might not know what will happen three plays later, but you canât just stand there saying you donât know where to throw the ball. You just have to play the game.
Similarly, a founder or manager will always feel that they have too little information to make a decision. Often, they will say they need additional studies, analyses, etc. But this isnât what your employees want to hear; they need to hear a clear yes or no. And this same principle applies to investors.8) Focus on Opportunities, not Problems
Problems never pay out in the long run as much as opportunities do. So donât let problems consume so much of your time that youâre unable to do anything else because of them.9) Organise your Time
As a founder, time can be your most precious resource or your worst enemy. If you donât take control of your time, it will take control of you and you wonât manage anything. Make sure you reserve time to clear your head, even if others want something from you.10) Finish your Work
Too many people start fifty things and never finish anything. A prerequisite of successful founders is following through.11) Enjoy What You Do
If you do something because someone else wants you to do it, or if you do it for money, you can do that for a month or two. But not for ten years. Work is hard, success is harder, so to make it worth it you had better enjoy what youâre doing.12) Do not Search for Consensus
It might sound counterintuitive, but companies that always act in consensus sometimes find themselves in a complicated situation. This is because they are missing disharmony, which can be a driving force for doing things differently or better. It is important to create some disharmony, because this gives birth to new ideas and innovations.13) Let the Ship Sail
If you have started a certain project, or set your teams on a specific course, do not regret it the next morning and change it; see it through.14) Energise your Team
Really working with your employees is critically important. A founder should be in touch with their team, motivating them and sometimes sharing common activities (I love to go cycling with members of my team.) This not only keeps your team motivated and makes them feel lead rather than commanded, it keeps you in touch with the pulse of your company in a way that you just donât get otherwise. Iâve known many managers that didnât talk to or interact with anyone on their team, and then were surprised then they finally found out their team had broken up.15) Measure Everything
If you do not measure, you will get nowhere. Good founders and managers should use metrics to understand how they are performing, although it is sometimes difficult to find the right people for the problem. Even using the wrong metrics can provide valuable lessons, and as you move forward you can reevaluate what you measure.16) Be True to Yourselves
If a person becomes a founder, they should remain true to themselves and should not pretend to be anything else. Itâs impossible to live under a false pretence for any significant length of time; inevitably, people will see through to the real you. So make sure the real you is worthy of of being seen.
A version of this article was originally published in the Czech edition of Forbes Online.
The retail industry has reached a tipping point where retailers can â and should â leverage advanced analytics to improve the overall consumer buying experience. The latest research from the McKinsey Global Institute estimates that retailers that leverage data analytics at scale can grow their profits by a whopping 60%. But even though retailers are generating more data than ever, itâs not enough to simply gather stockpiles of data and send it to the CIO. Rather, retailers need systems that can consolidate data from multiple sources, transform it into powerful insights that can inform tangible business actions, and get those insights into the hands of the people that need them.âThe idea is to capture a single 360-degree view of todayâs multichannel shopper to inform smart merchandising and marketing decisions. Retailers that donât get this right are leaving money on the table: Multichannel shoppers, whose ranks are growing, spend more than single-channel shoppers.â Retail TouchPoints
These are just a few of the insights contained in our latest eBook, â10 Ways to Drive ROI With Distributed Analytics: The Power of Data Collaboration For Suppliers, Managers And HQ.â We partnered with Retail TouchPoints to uncover the top ten ways that retailers can benefit from distributed analytics to drive increased revenue, including:
- Bringing newfound scale and accuracy to market- basket analysis
- Delivering personalized products and experiences to shoppers
- Tapping into new revenue streams with vendor partners
- Boosting the effectiveness of social-media marketing
- Turning heightened visibility into consumersâ omnichannel shopping journeys into top- and bottom-line gains
We also sat down with Retail Touchpoints for a webinar, where we discussed how smart retailers are harnessing distributed analytics unlock the full value contained in their data and business networks. The webinar covers data challenges and trends in the retail industry, using analytics to drive customer retention and enhance your customer experience, how your internal data can create valuable opportunities with external partners, and much more.
Todayâs CIOs and senior IT leaders need actionable advice to drive digital to the core of their products, processes, and talent âŠ while also preparing for disruptive trends that can deliver long-term business benefits. At the Gartner Symposium/ITxpo, coming up October 16â20 in Orlando, hundreds of attendees will get the best of both worlds as they discover how to make digital a core competence across the organization.
Built around the theme Lead 360: Drive Digital to the Core, this yearâs event features six tracks, spanning technology and information, leadership, and business strategy, plus industry-specific presentations focused on healthcare, finance, retail, and other key sectors. Keynote speakers will include former Secretary of Defense Robert Gates, Microsoft CEO Satya Nadella, and Cisco CEO Chuck Robbins.
If youâre attending, be sure to catch my presentation, âTurning Data Into Profit and Accelerating Digital Transformation,â on Tuesday (10/18) at 1:45 pm in the Dolphin Hotel Southern Room. Iâll be showing you how to accelerate your company's digital transformationâand boost its bottom lineâby creating and distributing data products and Smart Business Applications to your B2B networks.
Always backing me up, the GoodData team will also be on site at ITxpo! Come visit us at Booth 965 in the Dolphin Hotel Pacific Hall and learn how GoodData can help you commercialize and monetize your data and analytics. If youâd like to reserve a time to chat with us, just fill out this quick form and weâll be in touch to schedule a meeting. Hereâs a sneak peek at what weâll be talking about:Â
See you in Orlando!
On October 23â26, more than 10,000 leaders, innovators, and disruptors from across the financial services industry â including more than 1,000 CEOs â will gather in Las Vegas to explore the latest innovations in financial and banking technology at Money20/20.
Over four days, financial and banking executives will enjoy an exemplary experience highlighting the boldest and the best of the industry. This yearâs keynote speaker lineup executives from leading companies such as Visa, Stripe, Square, Google, Forbes Media, and Bank of America. Attendees can choose from more than 20 tracks covering leading-edge topics such as Data Analytics & Algorithm-Based Innovation, Global, Real-Time & X-Border Payments, Mobile Wallets & Payments, and Risk, Security & Fraud.
GoodData is proud to be a sponsor of Money20/20 2016, and we look forward to meeting you there! Come see me and the rest of the team at booth 1344 to see how leading financial service and payment processing companies are currently leveraging the GoodData platform. Hereâs a sneak peek at what weâll be talking about:Â
Itâs going to be a busy four days, so I recommend reserving a time to meet with the team. Click here to enter your request, and weâll be in touch to schedule your slot.
See you in Vegas!
Just months after MediGain won a Nucleus Research Technology ROI Award, another GoodData customer has disrupted their industry. Headquartered in London, Fourth is a leading provider of cloud-based cost control solutions to the hospitality industry. When the company partnered with GoodData to add an analytics product to its offerings, it uncovered not only a unique way to deliver additional value to customers, but also a profitable new revenue stream.
In an ROI case study on Fourth, Nucleus Research reports that the company needed a distributed analytics solution that could consolidate customer data from diverse geographic locations and put it into the hands of decision makers at multiple levels. GoodData met all Fourthâs requirements, including
- A focus on business line operations that lends itself well to the hospitality industry
- Ability to distribute customized reporting to their entire business ecosystem
- Easy deployment and scalability through Customer Lifecycle Management
- Easy sharing and embedding of the platform to eliminate geographic and organizational hurdles
- Integration with third-party systems
Fourth Analytics launched in 2013 as a subscription-based service, expanding Fourthâs product offerings and opening new revenue streams. The company benefits from previously untapped revenue opportunities, and its customers benefit from a more comprehensive view of their operations â a capability previously unheard of for the vast majority in the hospitality industry.
According to Nucleusâ calculations, Fourth achieved an ROI of 117 percent over a payback period of 2.7 years, with an average annual benefit of ÂŁ169,833. Thanks to its partnership with GoodData, the company has successfully disrupted the hospitality industry by introducing a one-of-a-kind product that builds its bottom line while enhancing customer relationships.
Unveiling the Possibilities for Distributed Analytics in the Financial World: GoodData at FinovateFall 2016
Last week we had the pleasure of representing GoodData at the FinovateFall 2016 conference in New York, a showcase of the best and most innovative new financial and banking technologies.
I have to say the level of energy and excitement among the Finovate attendees was impressive. From morning to evening, our booth saw a constant flow of visitors, among them many senior executives from top financial services companies. Some had scheduled appointments ahead of time, some were drawn to our new financial services video, and some had simply heard the buzz about what was going on at the GoodData booth:
Here you can see our team hard at work, giving personal attention to everyone who stopped by:
And hereâs Blaine Mathieu and Marco on stage presenting our 7-minute demo to a packed room â about 1,600 attendees!
Hereâs another demo shot, this one of Blaine playing âfront manâ while Marco rocked the demo:
Our time at FinovateFall 2016 was a whirlwind, and I can hardly believe itâs already behind us. We were so impressed with the quality of attendees, the level of expertise among presenters, and the high energy of the rapid-fire agenda that we canât wait for next year!
At GoodData, we believe that the power of enterprise data goes far beyond simply making better decisions. We believe that data is valuable, and our mission is to help enterprises transform their data into a profit center.
âGoodData, somewhat uniquely, is focusing on the embedding and monetizing of analytics, transforming it from cottage industry to a mainstream profitable production activity,â said Butler Analytics founder Martin Butler in his post âGoodData â Pioneering Production Analytics.â Butler asserts that if data is to be useful, it must be integrated into what he calls the âproduction environmentâ:âAnalytics need to be part of the production environment, and that means they need to be embedded within production applications â sales, purchasing, accounts, HR, production â and so on. But there is also something else. The terabytes of data most businesses have acquired, at considerable cost, represent an asset in their own right. This means the data can be used to generate revenue and profits. Why else would be call it an asset?â
The number of employees in any organization who need direct access to analytical tools, Butler contends, is actually quite small â sometimes no more than a few hundred in the largest corporations. However, the number of people who potentially benefit from embedded analytics can easily number in the tens of thousands. This is just one level at which GoodData offers unique value: through embedded analytics that offer mission-critical insights, when and where the user needs them.
Butler contends that the next level of analytics is the ability to use internal and external data assets to create revenue-generating data products.
âThe people who will pay for this information,â Butler writes, âmight be partners (a distribution network for example), suppliers and any other agents who might be able to profitably use the information services a business delivers.â
But itâs at a third level â what Butler calls âone of the most exciting developments in recent yearsâ â that GoodData truly distinguishes itself by acting âas a data middle-man, bringing organizations together who work in related industries so they can create much higher value information products.â
By collaborating and sharing data, complementary organizations can accomplish together what no one business could do alone, offering high-level data products from which a broad audience can benefit. And as Butler notes, these concepts have the potential to transform the business analytics industry:The winners in any industry are always those that do something different. In the business analytics space it is GoodData that has moved away from the catchy, but largely meaningless marketing cliches such as âself-serviceâ, âease-of-useâ, âbig dataâ and others that are starting to lose their sparkle â simply because they are not delivering. Having spent a decade in a hype wilderness, businesses can now start to production-line their analytics â the real work begins.
We couldnât have said it better ourselves.
Read the full article here.
Video has become an essential piece of the retail buying journey. More effective than text or static pictures alone when it comes to explaining features and benefits, customers are starting to expect quality video content when purchasing or researching products. But while content may be king, any video campaign requires accurate measurement in the form of analytics in order to optimize for success. Eliot Towb, a product manager for GoodData client Invodo, shared some key insights on video analytics in his article âWhy Online Video is Essential to Omnichannel Success.â
âYou must have analytics in place to get the most out of your video program,â Towb writes. âWithout analytics, you will not be able to detect site issues or changes that reduce the impact of video, and ultimately, the increased conversion that follows from a well-executed program.â
Towb goes on to present three sets of analytics that can help online retailers maximize revenues from their video content:
- Engagement metrics (View Rate, Completion Rate, Sharing) that tell the retailer how often and how long customers are interacting with its videos, as well as how likely they were to share videos with their communities.
- Satisfaction metrics (Ratings, Comments) let retailers know whether customers found the content helpful and how well it met other expectations. âComments are especially valuable for product videos in determining whether you have the right feature content in your videos,â Towb notes.
- Conversion metrics (Cart, Purchase Activity) represent the impact the retailerâs video content has on their bottom line.
In October 2015, GoodData kicked off its Good People Doing Good program where employees could take time off during their workday to volunteer for various causes around the city. It gave coworkers from different departments a reason to connect and work together in a non-business environment. The Company coordinated different types of events so that everyone could find something they were interested in. There was park cleanup for the environmental nut, soup kitchens for kind souls, house building for handy employees, and dog shelters for the animal lovers.
Our kick-off event was an environmental cleanup effort at Sue Bierman Park, a recreation area right in front of the Ferry Building. We met up as the sun rose and enjoyed some coffee and snacks with new friends before getting to work.
The group was split into two â one helped plant new foliage, and the other removed invasive shrubbery. In the end, we reunited to clip unwanted offshoots from the trees.
It was a great day outdoors where we were able to make friends from different departments and help create a better environment for the city.
We also volunteered at Project Open Hand, a soup kitchen that serves underprivileged individuals in the community. The history of the organization is fascinating; it dates back to the AIDs epidemic when a volunteer at Meals on Wheels saw how badly HIV patients were being treated at various hospitals and how they were shunned by society.
The founder created Project Open Hand as a way to shatter society's perception of these patients, and help lift their spirits in their time of need. Hearing this history gave us a sense of purpose, and it made the volunteer work a lot more fun and motivating.
We were split up into three groups: the kitchen, the warehouse, and the office. The kitchen crew cut fruits and vegetables to be cooked and served, the warehouse crew filled granola bags and sorted fruit to ensure the freshest meals, and the office crew wrote letters to donors to ensure continuing contributions.
There was a project for each individual's interests and skills, and everyone had a great time working together.Â Â
The Company wanted to make sure that everyone who wanted to volunteer had the proper outlet to do so. Thus, we signed up to build houses with Habitat for Humanity, an organization that provides low income housing for local residents in need.
Our group split four responsibilities: flooring, sound/fire proofing, scaffolding, and plumbing. The flooring group nailed boards on beams to create the ground of the second story in the house while the sound/fire proofing team put materials against the side of the wall to insulate sound. The scaffolding group built new scaffolds for workers and volunteers to stand on while working and the plumbing group fixed pipes to support running water.Â Â Â
Although it was hard work, everyone had a great time trying his or her hand at building a house.
Aside from the working with tools, we also wanted to do something for those animal lovers in the Company. Thus, we volunteered at Family Dog Rescue, which is a wonderful organization that saves unwanted dogs from extermination, and helps them find loving families.
The GoodData team helped the staff clean up the facilities at the shelter to keep the dogs nice and comfortable. Afterward, we had free time with the dogs where we walked them around the neighborhood, snuggled with them, and played catch with them in the yard.Â Â
Overall, the Good People Doing Good program has been a tremendous success, with participation from the whole company. We believe that volunteering will continue to be a large part of our company culture, and cannot wait for the next event!
âLetâs be clear about what we want from business analytics,â writes Butler Analytics founder Martin Butler in his post âStrategies for Profitable Business Analytics,â based on insights from our CEO Roman Stanek. âWe need more accurate and more timely decisions that cost less to process â and thatâs it.â
The founder of Butler Analytics goes on to examine the three ways in which organizations can operationalize business analytics:
- More efficient and effective business decisions that lead to tangible business actions. By understanding the decisions our people routinely make and the analytics needed to support these decisions, we can see how our data systems contribute to the top and bottom line.
- Clearer understanding of customersâ and business partnersâ decision-making processes. Organizations can monetize their data by creating products that address the uncertainties of customers, trading partners, suppliers and whoever might find the information useful.
- Merging external data sources, particularly those of businesses in related markets. For example, airlines, hotels, and car rental agencies can merge data to create snapshots of customer behavior that are more accurate and more useful than a single business might be able to create.
Butler concludes by citing the need for âa production oriented approach, where profitable business analytics can be realized.â If we continue to act as if analytics deliver value in their own right, he says, we risk âa gradual disillusionment with business analytics technology, and missed business opportunities.â