{"id":1403,"date":"2019-02-15T10:32:08","date_gmt":"2019-02-15T10:32:08","guid":{"rendered":"http:\/\/kusuaks7\/?p=1008"},"modified":"2023-07-10T12:55:37","modified_gmt":"2023-07-10T12:55:37","slug":"five-ways-your-data-strategy-can-fail","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/five-ways-your-data-strategy-can-fail\/","title":{"rendered":"Five Ways Your Data Strategy Can Fail"},"content":{"rendered":"

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There are plenty of great ideas and techniques in the data space: from analytics to machine learning to data-driven decision making to improving data quality. Some of these ideas that have been around for a long time and are fully vetted, proving themselves again and again. Others have enjoyed wide socialization in the business, popular, and technical press. Indeed,\u00a0The Economist<\/em><\/a>\u00a0proclaimed that data are now \u201cthe world\u2019s most valuable asset.\u201d<\/p>\n

With all these success stories and such a heady reputation, one might expect to see companies trumpeting sustained revenue growth, permanent reductions in cost structures, dramatic improvements in customer satisfaction, and other benefits.\u00a0 Except for very few, this hasn\u2019t happened. Paradoxically, \u201cdata\u201d appear everywhere but on the balance sheet and income statement. Indeed, the cold reality is that for most, progress is agonizingly slow.<\/p>\n

It takes a lot to succeed with data. As the figure below depicts, a company must perform solid work on five components, each reasonably aligned with the other four. Missing any of these elements compromises the total effort.<\/p>\n

<\/p>\n

 <\/p>\n

Let\u2019s explore each component in turn.<\/p>\n

Quite obviously, to succeed in the data space, companies need\u00a0data<\/strong>, properly defined, relevant to the tasks at hand, structured such that it is easy to find and understand, and of high-enough quality that it can be trusted.\u00a0It helps if some of the data are \u201cproprietary,\u201d meaning that you have sole ownership of or access to them<\/a>.<\/p>\n

For most companies, data is a real problem. The data is scattered in silos \u2014 stuck in departmental systems that don\u2019t talk well with one another, the\u00a0quality is poor<\/a>, and\u00a0the associated costs are high<\/a>. Bad data makes it nearly impossible to become data-driven and adds enormous uncertainty to technological progress,\u00a0including machine learning<\/a>\u00a0and digitization.<\/p>\n

Then, companies need a\u00a0means to monetize that data<\/strong>, essentially a business model for putting the data to work,\u00a0<\/strong>at profit. This is where selling the data directly, building it into products and services, using it as input for analytics, and making better decisions come to the fore. There are so many ways to put data to work that it is hard to select the best ones. A high-level direction such as \u201cusing analytics wherever possible\u201d is not enough. You have to define how you plan to use analytics to create business advantage and then execute. Without a clear, top-down business direction, people, teams, and entire departments go off on their own. There is lots of activity but little sustained benefit.<\/p>\n

Organizational capabilities\u00a0<\/strong>include talent, structure, and culture. Some years ago, I noted that most organizations were singularly \u201cunfit for data<\/a>.\u201d They lack the talent they need, they assign the wrong people to deal with quality, organizational silos make data sharing difficult, and while they may claim that \u201cdata is our most important asset,\u201d they don\u2019t treat it that way. If anything, this problem has grown more acute.<\/p>\n

Start with talent. It is obvious enough that if you want to push the frontiers of machine learning, you need a few world-class data scientists. Less obvious is the need for people who can rationalize business processes, build predictive models into them, and integrate the new technologies into the old. More generally, it is easy to bewail the shortage of top-flight technical talent, but just as important are skills up and down the organization chart, the management ability to pull it all together, and the leadership to drive execution at scale. Consider this example: Many companies see enormous potential in data-driven decision making. But to pursue such an objective, you have to teach people how to use data effectively (HBR\u2019s current\u00a0series on data skills<\/a>\u00a0will focus on this topic). Leadership must realize that earning even a fraction of the value data offer takes more than simply bolting an AI program into one department or asking IT to digitize operations.<\/p>\n

Structure and culture are also a concern. As noted, organizational silos make it difficult to share data, effectively limiting the scope of the effort. All organizations claim that they value data, but their leaders are hard-pressed to answer basic questions such as, \u201cWhich data is most important?\u201d \u201cHow do you plan to make money from your data?\u201d or \u201cDo you have anything that is proprietary?\u201d Some even refer to data as \u201cexhaust\u201d \u2014 the antithesis of a valued asset! Without an abundance of talent and an organizational structure and culture that value data, it is difficult for companies to grow successful efforts beyond the team and department levels.<\/p>\n

Fourth, companies need\u00a0technologies to deliver at scale and low cost.\u00a0<\/strong>Here, I include both basic storage, processing, and communications technologies, as well as the more sophisticated architectures, analysis tools, and cognitive technologies that are the engines of monetization.<\/p>\n

Quite obviously companies need technology \u2014 you simply can\u2019t scale and deliver without it. Facebook, Amazon, Netflix, and Google, who have succeeded with data, have built powerful platforms. Perhaps for these reasons, most companies begin their forays into the data space with technology. But from my vantage point, too many companies expect too much of technology, falling into the trap of viewing it as the primary driver of success. Technology is only one component.<\/p>\n

The last element is\u00a0defense<\/strong>, essentially\u00a0minimizing risk<\/a>. Defense includes actions such as following the law and regulations, keeping valued data safe from loss or theft, meeting privacy requirements, maintaining relationships with customers, matching the moves of a nimble competitor, staying in front of a better-funded behemoth, and steering clear of legal and regulatory actions that stem from monopoly power. You\u2019re unlikely to make much money from defense, but poor defense can cost you a lot of time, money, and trouble.<\/p>\n

Thus, data require a range of concerted effort. At a minimum, HR must find new talent and train everyone in the organization, tech departments must bring in new technologies and integrate them into existing infrastructures, privacy and security professionals must develop new policies and reach deep into the organization to enforce them, line organizations must deal with incredible disruption, everyone must contribute to data quality efforts, and leaders must set off in new, unfamiliar directions.\u00a0 Adding to complications, data, technology, and people are very different sorts of assets, requiring different management styles. It\u2019s a challenging transition. Many companies have tried to resolve their data quality issues with the latest technology as a shortcut (e.g., enterprise systems, data warehouses, cloud, blockchain), but these new systems have missed the mark.<\/p>\n

It is important to remember that the goal is not simply to get all you can out of your data. Rather, you want to leverage your data in ways that create new growth, cut waste, increase customer satisfaction, or otherwise improve company performance.\u00a0And \u201cdata\u201d may present your best chance of achieving such goals<\/a>. Successful data programs require concerted, sustained, properly-informed, and coordinated effort.<\/p>\n

Originally appeared in Harvard Business Review<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

There are plenty of great ideas and techniques in the data space: from analytics to machine learning to data-driven decision making to improving data quality. One might expect to see companies trumpeting sustained revenue growth, permanent reductions in cost structures, dramatic improvements in customer satisfaction, and other benefits.  Except for very few, this hasn’t happened. It takes a lot to succeed with data. As the figure below depicts, a company must perform solid work on five components, each reasonably aligned with the other four. Missing any of these elements compromises the total effort.<\/p>\n","protected":false},"author":388,"featured_media":3303,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[95],"ppma_author":[2937],"class_list":["post-1403","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data-amp-technology"],"authors":[{"term_id":2937,"user_id":388,"is_guest":0,"slug":"thomas-c-redman","display_name":"Thomas Redman","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Redman","first_name":"Thomas","job_title":"","description":"Dr. Thomas C. Redman, “the Data Doc,”<\/strong> President of Data Quality Solutions<\/a>, helps start-ups and multinationals; senior executives, Chief Data Officers, and leaders buried deep in their organizations, chart their courses to data-driven futures, with special emphasis on quality and analytics. He has a Ph.D. in Statistics and two patents."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1403","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/388"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1403"}],"version-history":[{"count":3,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1403\/revisions"}],"predecessor-version":[{"id":29093,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1403\/revisions\/29093"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3303"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1403"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1403"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1403"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1403"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}