{"id":677,"date":"2022-12-16T07:46:08","date_gmt":"2022-12-16T07:46:08","guid":{"rendered":"https:\/\/d3mlabs.de\/?p=677"},"modified":"2023-05-02T05:51:12","modified_gmt":"2023-05-02T05:51:12","slug":"bridging-the-gap-between-data-and-money","status":"publish","type":"post","link":"https:\/\/d3mlabs.de\/?p=677","title":{"rendered":"Bridging the gap between data and money"},"content":{"rendered":"\n

This article was co-authored by Elizabeth Press and Peter Schroeter<\/strong><\/p>\n\n\n\n

Data is a top priority on almost every C-suite agenda, and for good reason.<\/p>\n\n\n\n

When data is properly sourced, compiled, and understood, it has the potential to add tremendous value to a company\u2019s profitability and competitive positioning.<\/p>\n\n\n\n

However, without proper business acumen, data organizations often fail to control their costs, and are far less likely to realize return on their data investments.<\/p>\n\n\n\n

How can organizations effectively generate financial return from their data? And what are some common mistakes that companies make when trying to do so?<\/p>\n\n\n\n

I recently co-hosted a Harnham LIVE<\/a> LinkedIn event with Harnham\u2019s Peter Schroeter<\/a><\/strong> to answer these questions and more. Here\u2019s a summary of our chat below.<\/p>\n\n\n\n

Setting Clear Data Objectives is Key<\/strong><\/p>\n\n\n\n

When building data teams, many companies underestimate the effort that\u2019s involved in garnering valuable insights from data.<\/p>\n\n\n\n

There seems to be a common misconception that, if a company invests in the right tools and hires a bunch of smart people who understand the \u201cmodern data stack,\u201d then that company will realize a positive ROI on its investments in data.<\/p>\n\n\n\n

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What executives often forget is that, if a business doesn\u2019t have clear objectives on how its data can help make money, investing in data can end up having a negative impact on profit.<\/em><\/p>\n<\/blockquote>\n\n\n\n

Therefore, the path to positive ROI in data starts with identifying how data supports the business strategy and how it can accelerate monetization.<\/p>\n\n\n\n

To make sure their data teams are set up for success, companies must plan for enough positive cashflow impact to justify their investment in data.<\/p>\n\n\n\n

Requisite Skills Go Beyond Tech<\/strong><\/p>\n\n\n\n

Once clear objectives have been set, companies need to ensure their data teams and data products stay on track.<\/p>\n\n\n\n

This requires a few things. One of the most important ways to ensure revenue and data goals are aligned is to build a well-rounded team. That means building a team that has a mix of technical knowledge and business acumen.<\/p>\n\n\n\n

Of course, it can be challenging to find data professionals that are also business experts. Luckily, it\u2019s more important to have a team<\/em> that\u2019s diverse rather individual employees that \u201chave it all.\u201d<\/p>\n\n\n\n

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When building data teams, a common mistake companies make is hiring a technical person who says they know business, rather than hiring a person with a solid business background.<\/em><\/p>\n<\/blockquote>\n\n\n\n

Data is not a purely technical domain. Thus, in-depth knowledge in areas beyond code and tools is important. Skills in business, strategy, project management, and communication, as well as process expertise are critical to manage a data organization successfully. Security, compliance, and privacy are also becoming non-negotiables.<\/p>\n\n\n\n

Keep It Lean<\/strong><\/p>\n\n\n\n

An efficient data ecosystem is also important if companies want their data to generate a positive ROI.<\/p>\n\n\n\n

Lean processes with a focus on governance, data quality, and observability will create a more robust data ecosystem that has a lower chance of producing incomplete, incorrect, or unfocused data, especially if a company is just starting out in the data space.<\/p>\n\n\n\n

Focus on Break-Even Points and Payback Periods<\/strong><\/p>\n\n\n\n

Initial investments in cloud technology and other critical steps are expensive. Think in terms of break-even points and time horizons when projecting the investment payback period.<\/p>\n\n\n\n

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Being realistic about a payback period and decisive about an effective monetization strategy are critical for realizing return on long-term investment in data.<\/em><\/p>\n<\/blockquote>\n\n\n\n

Over time, data has the power to make valuable change in an organization\u2014if it\u2019s harnessed in the right way.<\/p>\n\n\n\n

For more information about the connection between data and money, you can watch a recording of our <\/em>entire live discussion<\/em><\/a> on Harnham\u2019s LinkedIn.<\/em><\/p>\n\n\n\n

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This article was also published by Harnham<\/a><\/p>\n\n\n\n

Relevant D3M Labs articles<\/strong><\/p>\n\n\n\n

<\/p>\n\n\n\n

Data only has financial value if it can be monetized – An interview with Michael Guthammar<\/a><\/p>\n\n\n\n

What I look for when I hire a data professional<\/a><\/p>\n\n\n\n

Lehren aus New York: Es gibt nie „nur Business“<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

This article was co-authored by Elizabeth Press and Peter Schroeter Data is a top priority on almost…<\/p>\n","protected":false},"author":1,"featured_media":682,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[51,10,15,33,8],"tags":[],"class_list":["post-677","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business","category-data-leadership","category-data-strategy","category-hiring","category-strategy","wpcat-51-id","wpcat-10-id","wpcat-15-id","wpcat-33-id","wpcat-8-id"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t