{"id":7246,"date":"2024-01-17T10:24:20","date_gmt":"2024-01-17T10:24:20","guid":{"rendered":"https:\/\/members.dataiq.global\/?post_type=article&p=7246"},"modified":"2024-06-24T13:11:52","modified_gmt":"2024-06-24T12:11:52","slug":"silos-and-dashboards-the-voyage-of-data-foundations-to-influence-todays-business-decisions-for-a-better-tomorrow","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/silos-and-dashboards-the-voyage-of-data-foundations-to-influence-todays-business-decisions-for-a-better-tomorrow\/","title":{"rendered":"Silos and dashboards: Mars’ voyage of data foundations to influence today\u2019s business decisions for a better tomorrow"},"content":{"rendered":"

In an era where adoption of the latest tools and investment in the most recent technological solutions can mean the difference between beating the competition or floundering in the wake of others, Mars has demonstrated that the power of data can be leveraged to achieve success, competitive advantage and make a real difference. \u00a0<\/span>\u00a0<\/span><\/p>\n

The data leadership team at Mars takes a pragmatic and proactive approach to examining and identifying issues across the business and then implementing a range of solutions to deliver value such as by rectifying bottlenecks that arise. In addition to investing in leading platform technology such as from Databricks, through focusing on building a strong data culture with the use of storytelling, and a steady stream of data literacy education, Mars has rapidly advanced its data capabilities and improved the standing of data in the business.<\/span>\u00a0<\/span><\/p>\n

DataIQ has been provided a unique insight into the data transformation journey at Mars \u2013 one of its <\/span>global members<\/span><\/a> \u2013 through talking to Deepak Jose, Global Head of One Demand Data and Analytics (ODDA) Solutions, Mars, and No. 4 in the DataIQ 100 USA 2023.<\/span><\/p>\n

\u00a0<\/span><\/p>\n

Context<\/span><\/b> in the world of Mars<\/span><\/h4>\n

Mars is a world-leading manufacturer of food and pet care with its products impacting millions of people and animals daily for more than a century across the globe, and the scope of this has been made possible through the power of data. The data being used by teams at Mars have kept the business at the front of the international food market for decades and is set to continue expanding the overall market share and customer offerings.\u00a0<\/span>\u00a0<\/span><\/p>\n

The leadership team at Mars is very aware of its impacts on the planet as a global business that leaves a footprint, which is why the organisational purpose has become \u2018the world we want tomorrow starts with how we do business today\u2019. Through the utilisation of data, Mars is creating a superior vision of its business operations and day-to-day dealings \u2013 from the sale of single chocolate bars to vast intercontinental supply chains. By leading the charge for change, Mars is setting the benchmark of how global businesses conduct themselves and evolve with changing customer needs.\u00a0<\/span>\u00a0<\/span><\/p>\n

Mars wants to achieve the highest possible level of customer service to keep ahead of competitors and to maintain a business focus on the customer, not just calculating success by metrics associated with profit. Through its One Demand solution \u2013 a data and analytics function at the centre of Mars\u2019s approach to data-driven performance \u2013 the data team at Mars has been able to build data products with a focus on holistic and integrated analytics that are supported by domain expertise.<\/span>\u00a0<\/span><\/p>\n

\u201cBy embracing data-led decision-making, we are moving away from a cost-centre mindset to a profit-centre mindset,\u201d said Jose. \u201cWe are determined to focus on the bigger business goals and ambitions while simultaneously improving the way our global operations interact with the environment and our customers.\u201d <\/span>\u00a0<\/span><\/p>\n

When describing how the issues faced by Mars were to be tackled, Jose stated that \u201cWe start with the business problem first, and a data and tech second mindset. We believe if you have a decision map mindset, you will be able to drive the biggest value for the organisation.\u201d<\/span>\u00a0<\/span><\/p>\n

The way that Mars is examining itself has been to break down data silos and leverage artificial intelligence (AI) capabilities to heighten decision-making. It has been broken down as follows:<\/span>\u00a0<\/span><\/p>\n

    \n
  • Analytics introduction<\/span>\u00a0<\/span><\/li>\n
  • Strategic framework<\/span>\u00a0<\/span><\/li>\n
  • Connected analytics<\/span>\u00a0<\/span><\/li>\n
  • Responsible generative AI<\/span>\u00a0<\/span><\/li>\n
  • Learnings<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

    \u00a0<\/span><\/p>\n

    Through this approach, the data team at Mars has been able to develop an integrated customer journey for its omnichannel operations that continue to improve customer offerings and fuel business growth.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Deepak Jose was listed as number four in the 2023 DataIQ 100 USA list: the only fully curated power list of the most influential data and analytics practitioners. Jose was awarded this distinction for his contribution to improvements in the data and analytics industry, his participation in driving forward to standing of the data office in organisations, and for his unwavering support of data-driven innovations. Since 2014, DataIQ has been tracking the rise of Chief Data Officers, Chief Analytics Officers, Data Scientists, data governance experts, and the leaders of key vendors and service providers. Inclusion in the DataIQ 100 is a notable badge of honour that is widely referenced by the individuals who make the cut.<\/span>\u00a0<\/span><\/p>\n

    One of the unique aspects of Mars is that it remains a family-owned business with an emphasis on cross-generational markets. There are legacy customers enjoying classic products as well as the opportunity to reach new customers through different brands in multiple different markets.<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Building the business and breaking silos<\/span><\/b>\u00a0<\/span><\/h4>\n

    To achieve excellence regarding customer service, Mars utilised its One Demand solution to maintain a competitive advantage over rival businesses and to accurately monitor success metrics. One Demand has heightened international connectivity and collaboration in a way that has not been possible previously.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Jose described the connectivity of a giant brand in a unique way: \u201cTo offer our integrated brand experience, the connectivity between all departments is important, but what happens in a large organisation? There is a famous example of four blind people explaining an elephant: one blind person is touching the trunk of the elephant and describes it as a snake, a second blind person is touching the leg and explaining that it is a stump, a third blind person is touching the tail and describing it as a furry mouse, and the fourth is touching the ear explaining it is like a sheet of leather. <\/span><\/p>\n

    “This story is a great example of a large organisation where the elephant is the consumer. A sales function might explain the consumer a certain way, a marketing function might explain the consumer in a slightly different way, supply chain function slightly different again, and this happens for all departments. The reason this occurs in large-scale businesses is because there are different sources of truth.”<\/span><\/p>\n

    If business functions were allowed to continue being siloed and uncommunicative, the different sources of truth would not travel and, over time, each siloed section would drift further from the other departments. This would create a huge number of difficulties for Mars, particularly when working on a global scale, and the costs incurred would steadily increase. <\/span><\/p>\n

    Furthermore, the data being collected would remain underutilised by all departments and the learnings from different data sets would never leave the individual silos in which they were created. By setting out to smash the silos that had developed and were steadily developing in Mars, the data team were able to massively increase the resources available between functions and departments which led to numerous enhancements and improvements across projects, operations, and data knowledge. <\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Addressing the issues<\/span><\/i><\/b>\u00a0<\/span><\/h4>\n

    By focusing on a business problem first, data and technology second mindset, the team at Mars was able to efficiently begin analysing existing issues and forming a plan of action. The Mars data office has evolved its capabilities and technologies over recent years by following the business problem first ideology. <\/span><\/p>\n

    Originally, the data office was running on a data warehouse model, this then evolved into a data lake and was followed by data lakehouses which may in turn develop into a data mesh approach in the future. By examining the business problems and then stripping everything back to the basic data foundations, the Mars data team generated new knowledge avenues and insights which were then utilised to combat the individual business problems in a proactive manner, rather than a reactive one. <\/span><\/p>\n

    Furthermore, the way in which data technology was integrated allowed for a level of flexibility for future technological advancements meant that Mars was able to rapidly and easily embrace new opportunities. It can be an additional cost at the time of installation to allow for flexibility, but the long-term successes it adds to business operations far outweigh the cost.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u201cWhen you understand and prioritise the business problem, you can prioritise the actions which will generate some value,\u201d said Jose. \u201cTo drive this, we can clearly understand what the knowledge is, and which insights are needed \u2013 the data and tech comes later. Having a business problem first and a data and tech second mindset has helped when it comes to building data products for us.\u201d<\/span>\u00a0<\/span><\/p>\n

    This approach may seem counterintuitive for a data professional, but the results of the business problem first approach have seen Mars tackle numerous issues on a truly global scale while being able to fuel its data capabilities. It has allowed the data team to combat silos in different nations and departments as well as improving communication between individuals and teams.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Integrated brand experiences<\/span><\/i><\/b>\u00a0<\/span><\/h4>\n

    One of the biggest issues that Mars has tackled is the idea of an integrated brand experience. The core aspect of this problem was increasing engagement with customers and ensuring that customers were receiving the highest level of care and satisfaction from Mars. Customers of Mars most commonly buy the products either through brick-and-mortar establishments or ecommerce sites \u2013 a combination called brick-and-click \u2013 which allows for a diverse array of interaction possibilities that the Mars data team noted were not being capitalised.\u00a0<\/span>\u00a0<\/span><\/p>\n

    As an example, a Mars product consumer may have a purpose to inspire them to purchase Mars products, such as Halloween. This in turn leads to the consumer thinking about shopping and then acting upon that thought by either visiting a brick-and-mortar shop or an ecommerce site to purchase the goods needed. Here, there are numerous additional opportunities to engage with customers that were being missed by Mars such as providing additional services and products that would heighten the experience for the customer.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u201cThe interesting part is each part of these brand experiences is owned by different parts of the organisation,\u201d said Jose, highlighting the need for silos to be smashed and communication to be improved. <\/span><\/p>\n

    \u201cThis is not a connected journey, sometimes it can be very non-linear \u2013 customers might get inspired, go to a shop, and then decide to buy; they might be consumed, then they might decide to go back and buy; or they might be planning and directly going to buy. The complexity of a non-linear consumer journey is very important for us to understand, and that is an important business problem to solve. To solve it with connected decision-making, we need to have a connected data foundation.\u201d <\/span>\u00a0<\/span><\/p>\n

    A connected data foundation allows businesses to operate multiple decision ecosystems, as long as the thought process always begins with identifying what type of insight is needed. Once this has been identified, an AI and analytics capability can then solve it efficiently and effectively.\u00a0<\/span>\u00a0<\/span><\/p>\n

    A connected data foundation means there is a single source of tools for the organisation \u2013 even a global one \u2013 which will power long-term success and adaptability for the future.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Disruption is needed<\/span><\/i><\/b>\u00a0<\/span><\/h4>\n

    A constantly recurring theme with breaking silos and creating a single source for an organisation is that disruption is needed. Without disruption the ongoing issues will remain embedded and continue to fester. Of course, disruption can be a frustrating and time-consuming process, but the medium- and long-term benefits vastly outweigh the short-term costs and inconveniences.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Previously, businesses built the operations around capabilities that would last and weather any storm that arrived \u2013 but now it is widely accepted that businesses need to build operations around capabilities that can adapt and be flexible to changing conditions.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u201cWhen we build out the capabilities for data products, our ability to make it interoperable, or to add new data sources has helped tremendously,\u201d said Jose. \u201cThe Covid-19 pandemic, which was a global disaster, changed consumer preferences dramatically. Ecommerce channels accelerated during global lockdowns, so Mars reacted to leverage a lot of changes in consumer behaviours and mindsets. This data was used to drive better decision-making for the organisation to emerge stronger from the problem being faced. Building to adapt is very important.\u201d<\/span>\u00a0<\/span><\/p>\n

    The adaptable capabilities that have been built by Mars fall into three areas:<\/span>\u00a0<\/span><\/p>\n

      \n
    • Strategy<\/span>\u00a0<\/span><\/li>\n
    • Planning<\/span>\u00a0<\/span><\/li>\n
    • Optimisation<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

      \u00a0<\/span><\/p>\n

      This was first put into practice in one geography for testing and proof of concept. Following extraordinary levels of success and improvements it was scaled up further, and eventually scaled globally.\u00a0<\/span>\u00a0<\/span><\/p>\n

      \u00a0<\/span><\/p>\n

      Enter Voyager<\/span><\/b>\u00a0<\/span><\/h4>\n

      Silos began to emerge in Mars\u2019s operations due to the vast scale of data being consumed across the globe. Real-time decisions regarding supply chains, portfolios, and digital selves were compounded by working with complex algorithms through ecommerce sites and over one million data points being assessed at once. <\/span><\/p>\n

      To navigate this, different departments and geographies began creating their own solutions which were slow, siloed, and often provided misguided actions for decisions. Ultimately, these processes were not sufficient and unsustainable for prolonged use, particularly in a time of rapidly evolving digital commerce.<\/span>\u00a0<\/span><\/p>\n

      Mars developed a data-backed solutions tool that operated on one single source of truth for digital commerce: Voyager. Voyager, which runs on Databricks, has been designed to follow the flexibility and business problem first approaches Mars had identified as key to success.\u00a0<\/span><\/p>\n

      The Voyager solution provides performance and insight tracking for numerous work streams including:<\/span>\u00a0<\/span><\/p>\n

        \n
      • Product and portfolio<\/span>\u00a0<\/span><\/li>\n
      • Market<\/span>\u00a0<\/span><\/li>\n
      • Supply chain<\/span>\u00a0<\/span><\/li>\n
      • Investment optimisation<\/span>\u00a0<\/span><\/li>\n
      • Search and category<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

        \u00a0<\/span><\/p>\n

        As a comprehensive AI powerhouse, Voyager has been designed to drive data-backed solutions for improved efficiency and effectiveness for digital commerce.\u00a0<\/span>\u00a0<\/span><\/p>\n

        Since its release, Voyager has seen an immediate impact on US-based business such as $3 million profitability enablement in 2023, the release of 20 hours or more for associates each week, heightened interconnectivity between Mars\u2019s One Demand partners, and a clear roadmap for scalability in different geographies. <\/span>\u00a0<\/span><\/p>\n

        \u201cThe Halloween season after the release of Voyager was the strongest Halloween Mars had ever experienced,\u201d said Jose.\u00a0<\/span>\u00a0<\/span><\/p>\n

        Furthermore, Mars has since been recognised as one of the most featured consumer packaged goods businesses because of its best-in-class content and retailer marketing experience provided to consumers.\u00a0<\/span><\/p>\n

        Data products hold the power to unlock incredible day-to-day value for different businesses. Mars has managed to achieve this by cementing the importance of three key principles which are supported by the Voyager solution:<\/span>\u00a0<\/span><\/p>\n

          \n
        1. Freedom: Businesses require freedom to define their future. They need profits to remain free. The top- and bottom-line growth impact a data product can make is arguably the most important thing.<\/span>\u00a0<\/span><\/li>\n
        2. Mutuality: Businesses do not want to create data products that nobody uses \u2013 it must be mutual. Data products help associates make rapid accurate decisions.<\/span><\/li>\n
        3. Efficiency: The data product should be flexible and continue improving the accuracy of the decision making.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

          \u00a0<\/span><\/p>\n

          Generative AI and the future at Mars<\/span><\/i><\/b><\/h4>\n

          In 2023 there has been an explosion of use and opportunities in the world of generative AI, and this shows no sign of slowing down. Mars has positioned itself to be in prime shape to embrace and adopt generative AI tools as its Voyager solution and data foundations have been designed to accommodate new technologies and avenues of opportunity, such as generative AI.\u00a0<\/span>\u00a0<\/span><\/p>\n

          \u201cDepending on the level of disruption and complexity, there are different capabilities that we are actively working on,\u201d said Jose. \u201cThe important thing for Mars, is the fact that responsible AI is non-negotiable. We have a corporate AI governance board writing an AI code of conduct to ensure there is fairness to the AI modes. This is where it comes to ethical collection of data \u2013 we have strict standards.\u201d<\/span>\u00a0<\/span><\/p>\n

          Generative AI is arguably the most recent example of innovation for businesses \u2013 particularly data-driven businesses \u2013 and as Mars has shown, innovation should be embraced from a business problem standpoint with technology and data as a secondary priority. Generative AI is opening the doors to a whole new field of possibilities and capabilities for businesses of all shapes and sizes across the globe, but it needs to focus on the business problems and the people that operate the solutions.\u00a0<\/span>\u00a0<\/span><\/p>\n

          \u201cThe most important aspect when it comes to capability is not about tools or data products \u2013 it is about people,\u201d said Jose. \u201cMars uses a honeycomb structure where tools are one part of the equation. But to drive systemic change, it is a combination of organisation, skills, and competencies; the processes, and more importantly the culture and mindset, should start from the top. When the leaders start asking data-driven questions, the associates will start giving data-driven answers.\u201d<\/span>\u00a0<\/span><\/p>\n

          By taking a multi-pronged approach to business issues that involved analysing the effectiveness of the data foundations, breaking silos, developing an organisation-wide dashboard and pivoting the business to embrace change and disruption, Mars has been able to thrive in a time of dynamic market change. The Mars data team has been instrumental in setting up success and positioning the business to be able to readily incorporate new technologies and operations for the foreseeable future, providing an unrivalled advantage over the competition simply by utilising these methods and mantras.\u00a0<\/span>\u00a0<\/span><\/p>\n

          Jose was keen to acknowledge that Mars would not be where it is today without Databricks: “Databricks are the unsung heroes in a true partnership between the two organisations.” Databricks technology supports all machine learning across Mars\u2019 huge consumer databases.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

          DataIQ spoke with Deepak Jose from Mars Wrigley to gain insight into the monumental data journey that has taken place in recent years and radically altered the business data capabilities.<\/p>\n","protected":false},"author":19,"featured_media":7247,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,282],"tags":[240,332,228,333,119,334,335,319,329,330,327,331,294,328,304],"pillar":[193,197,194],"class_list":["post-7246","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-member-case-studies","tag-case-study","tag-dashboard","tag-data-culture","tag-data-led","tag-data-literacy","tag-databricks","tag-environmentalism","tag-gen-ai","tag-global","tag-international","tag-mars","tag-retail","tag-silo","tag-supply-chain","tag-upskilling","pillar-strategy","pillar-technology","pillar-leadership"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-21 01:42:06","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/7246","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=7246"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/7246\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/7247"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=7246"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=7246"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=7246"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=7246"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}