{"id":13293,"date":"2017-02-24T15:00:20","date_gmt":"2017-02-24T14:00:20","guid":{"rendered":"http:\/\/timoelliott.com\/blog\/?p=13293"},"modified":"2021-08-18T17:16:43","modified_gmt":"2021-08-18T15:16:43","slug":"analytics-innovation-disruption-and-transformation","status":"publish","type":"post","link":"https:\/\/timoelliott.com\/blog\/2017\/02\/analytics-innovation-disruption-and-transformation.html","title":{"rendered":"Analytics Innovation, Disruption, And Transformation"},"content":{"rendered":"

I’m presenting a session on Analytics Innovation, Disruption, and Transformation<\/a> at the BI2017<\/a> conference in Orlando next week.\u00a0This post is an introduction to some of the themes I’ll be covering.<\/p>\n

Analytics has been around for a long time, and it\u2019s undergoing some major changes, but one thing never seems to change — it’s still the hottest technology space in the industry.<\/p>\n

For example, this is Gartner\u2019s list of the top strategic trends for 2017 \u2013 and you can see that \u201cintelligence\u201d is at the top of the list, including machine learning, intelligent apps, and intelligent things.<\/p>\n

\"\"<\/a>And BI & Analytics is yet again the top priority on Gartner\u2019s long-running annual CIO survey, as it has been for ten out of the last twelve years.<\/p>\n

\"\"<\/a><\/p>\n

Note that this is actually a very unusual situation. Normally, a new technology becomes a priority, companies invest money in it, and then it fades back down the list now that the new \u201cproblem\u201d has been solved. Mobile, for example: when smartphones and tablets came out, it suddenly became a high priority to support them, but now that the technology is in place, it has faded back down the list.<\/p>\n

So why has analytics stayed so high for so long? Why haven\u2019t we \u201cfixed it\u201d yet? One of the reasons that analytics remains a hot topic is that the amount and variety of data available has skyrocketed, constantly creating new analytic challenges. But even more importantly, analytics has become an essential part of digital transformation<\/em>.<\/p>\n

For the last few decades, we\u2019ve typically thought of business intelligence as a byproduct of our operational processes. In other words, we manufacture products, and ship them around the world, and sell them to customers. Each of these processes generates a lot of data, and we use that data to keep track of operations and create more optimized processes in the future.<\/p>\n

\"\"<\/a>This remains as true and important today as it\u2019s ever been in the past. But now there\u2019s another dimension coming into play. Organizations are increasingly realizing that digital transformation doesn\u2019t just require new processes \u2013 it requires a new approach to implementing<\/i> processes. They have to be more agile, more intelligent, and more responsive to change.<\/p>\n

\"\"<\/a>These new processes flip the traditional equation on its head. New digital processes are created on the fly<\/i>\u00a0by analytics<\/em>.<\/p>\n

The typical customer journey is a great example. In the old days, purchasing a product\u00a0was a fairly linear process, and\u00a0companies characterized as a \u201csales funnel\u201d. But now it\u2019s more like a \u201cwrite your own adventure\u201d book \u2013 where there are many different possible interaction paths. At each point in the process, the\u00a0customer gets to choose the next chapter.<\/p>\n

Analytics is used to help guide the customer towards the “right”\u00a0choice at each point, indicating what other products they may be interested in, or offering discounts to encourage immediate purchase.<\/p>\n

In other words, every \u201ccustomer process\u201d is unique, with analytics doing all the work, creating thousands or millions of personalized \u201cprocesses\u201d based on the needs of each individual.<\/p>\n

Because these new processes are analytics-powered rather than hard-coded, they can be much more agile and responsive to change \u2013 indeed, new machine learning approaches mean that they can even update and optimize themselves.<\/p>\n

Effectively creating and managing these kinds of flexible, on-the-fly processes is THE big new opportunity in digital business. But it also means that analytics has to have a more process-oriented approach, not just treated as a series of one-off decisions — and this is an area where traditional BI leaders\u00a0such as SAP have an advantage over the “islands of innovation” approach of tools that focus only on\u00a0data discovery and visualization.<\/p>\n

\"\"<\/a>Gartner believe that information and analytics will be used to reinvent, digitalize, or eliminate 80% of today\u2019s business processes.<\/p>\n

Analytics is no longer just an afterthought to the \u201dreal business\u201d \u2013 it\u2019s the heart of the new business models of the future.<\/strong><\/p>\n

Analytics also enables \u201clive businesses.\u201d A\u00a0live business is one that anticipates, simulates, and innovates new business opportunities, that looks to create the future, rather than just reporting on the past.<\/p>\n

\"\"<\/a>Here\u2019s the analogy I like to use. Imagine this expensive crystal vase has been knocked over and is plummeting to the floor. That\u2019s the equivalent of something going wrong in your business \u2013 an overdue production, a late delivery, or an unhappy customer.<\/p>\n

Traditionally, businesses would have been stuck with analyzing the puddle and shards of glass on the floor after <\/i>the vase had broken, in order to figure out what went wrong, who to blame, and how to avoid it next time.<\/p>\n

But what if you could actually catch the vase before<\/i> it hit the ground? In other words, if you knew about the business problem before you lost money or ruined customer satisfaction? That’s\u00a0<\/em>live business. To achieve this, you need a seamless, real-time link between operations and analytics \u2013 and this plays to the strengths of new in-memory operational+analytics solutions such as the SAP HANA platform.<\/a><\/p>\n

But while analytics is a hot topic, it doesn\u2019t mean that it’s without problems. Various reports indicate that the reported success rate of BI deployments has stalled. For example, Howard Dresner found that BI initiatives described as \u2018successful\u2019 dropped from 41% to 35% in 2015<\/a>.<\/p>\n

It’s worth noting that it\u2019s not completely clear what BI \u201csuccess\u201d really means — it’s largely a subjective measure because few organizations actually define what success would look like before they start on a projects. But I believe user\u00a0satisfaction is\u00a0falling because business expectations are rising even faster than BI technology improvements.<\/p>\n

This has real consequences. In particular, some organizations continue to implement only \u201cold style\u201d, centralized business intelligence. This is increasingly is out of phase with the needs of today\u2019s more analytics-savvy business users. Gartner calls these people \u201cBI-nosaurs\u201d, and warn that the comet is coming that might wipe them out.<\/p>\n

Here are some of the typical complaints<\/a> of today’s analytics users.<\/p>\n

\"\"<\/a><\/p>\n

First, users find analytics\u00a0too slow, with almost a third having to wait days or weeks for a BI request. People would like to access information themselves without needing IT, but a third said that they find their enterprise BI too complex, too complicated, and too cumbersome to use. Finally, almost\u00a0half the data that business people want to access is now from outside the organization, and is therefore unlikely to be in the corporate system in the first place.<\/p>\n

\"\"<\/a>For all these reasons, the penetration rate of analytics remains low in organizations with many organizations reporting that fewer than 10% of employees using BI<\/a> \u2013 although again, it\u2019s not always clear what \u201cusing\u201d means \u2013 often the data is used indirectly, for example cut-and-pasted into a spreadsheet or presentation. But there\u2019s obviously a huge opportunity to get more data to more people, both inside and outside the organization.<\/p>\n

From the point of view of business users, traditional analytics organizations look like the taxi companies that have been displaced by more flexible car-sharing applications like Uber and Lyft. People always found taxis too expensive, and annoyingly hard to find when you wanted one \u2013 but there was no alternative, so they put up with it. Now there are lots of lightweight analytics products available, and business departments increasingly have their own IT budget to spend.<\/p>\n

The result it that the older ways of doing things are being disrupted, and just like the taxi companies, traditional analytics organizations have to adapt to the new tools and new ways of working in order to compete effectively.<\/p>\n

So how should analytics organizations react to these trends? I’ll be following up with future posts on areas such as supporting “modern BI”, the new big data architectures, the adoption of predictive and machine learning, and changes to how companies\u00a0are organizing for BI.<\/p>\n

To make sure you don’t miss those posts, you may want to consider subscribing by email to this blog<\/a>, or following me on twitter<\/a>!<\/p>\n","protected":false},"excerpt":{"rendered":"

Analytics has been around for a long time, and it’s still the hottest topic in the industry, but it’s undergoing some major changes… <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[27,100,160,172,173,204,213,324,1175,747,835,911,927,1216],"class_list":["post-13293","post","type-post","status-publish","format-standard","hentry","category-thoughts","tag-bigdata","tag-analytics","tag-bi","tag-bicc","tag-big-data","tag-business-intelligence","tag-businessobjects","tag-dashboards","tag-machine-learning","tag-modern-bi","tag-predictive","tag-sap","tag-sapanalytics","tag-success"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-3sp","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/13293","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=13293"}],"version-history":[{"count":3,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/13293\/revisions"}],"predecessor-version":[{"id":20630,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/13293\/revisions\/20630"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=13293"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=13293"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=13293"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}