{"id":5407,"date":"2021-04-27T18:17:59","date_gmt":"2021-04-27T17:17:59","guid":{"rendered":"https:\/\/www.theoaklandgroup.co.uk\/?p=5407"},"modified":"2024-02-15T11:33:54","modified_gmt":"2024-02-15T11:33:54","slug":"the-use-case-for-project-analytics","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/the-use-case-for-project-analytics\/","title":{"rendered":"The Use Case For Project Analytics"},"content":{"rendered":"<p><i><span data-contrast=\"none\">In this final article of the Project Analytics series, now that\u00a0<\/span><\/i><i><span data-contrast=\"none\">we\u2019ve<\/span><\/i><i><span data-contrast=\"none\">\u00a0covered how to launch your Project Analytics capability, we explore what each category of users will make of their newly created capabilities now the skills, processes and technology are finally in place.<\/span><\/i><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">There are typically four basic use cases that we observe when building out a Project Analytics capability:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">1. Report Consumers<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">2. Analysts<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">3. Partners\/Advanced Analysts<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">4. Machine Learning<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Use Cases for Report Consumers:\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">By this stage, report consumers are likely to be loving the analytics and reporting capabilities you have created. They should now have fast, high-quality reports and analysis related to the specialist insights they need.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">It should be relatively easy to find use cases for this group simply by exploring the information you already report on today. The reports you already produce are most likely helpful but perhaps in need of improvement.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">However, it may not always be that straightforward to perform a straight enhancement of your existing reports for several reasons:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"none\">Extracting data from its source (without adjustment) will invariably have issues. By automating data pipelines, <\/span><span data-contrast=\"none\">you&#8217;ve<\/span><span data-contrast=\"none\">\u00a0removed the ability to manipulate and finesse the data manually, which places greater pressure on resolving any data quality issues further upstream.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Adding commentary to your reporting\/analytics output becomes more challenging because the reporting tools\u00a0<\/span><span data-contrast=\"none\">don&#8217;t<\/span><span data-contrast=\"none\">\u00a0give you the same flexibility as the previous &#8216;hand-cranked&#8217; reports your team may have created in a spreadsheet or\u00a0<\/span><span data-contrast=\"none\">Powerpoint<\/span><span data-contrast=\"none\">\u00a0deck.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Report proliferation&#8217; can be another challenge. Now that you can report on everything, there is often a temptation to create endless dashboards and reports that slice and dice the data in an infinite variety of ways. The lesson here is to keep your outputs simple and constrain the variations at the outset.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\">Finally, be mindful of how your reports are commissioned and version-controlled to ensure quality. You need to strike a balance between allowing business users to create their own reports or waiting for the analytics team to produce reports (but potentially becoming a bottleneck).<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">The moral here is that &#8216;reporting democratisation&#8217; can sound like a great idea, but it can turn and bite you if left unchecked. The whole argument for &#8216;self-service&#8217; reporting is more of an operational and cultural challenge than a technical one.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Use Cases for Analysts:\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">By this stage, your analysts should be delighted with the project analytics capabilities\u00a0<\/span><span data-contrast=\"none\">you&#8217;ve<\/span><span data-contrast=\"none\">\u00a0created. However, as adoption increases,\u00a0<\/span><span data-contrast=\"none\">you&#8217;ll<\/span><span data-contrast=\"none\">\u00a0need to think carefully about what controls you need to put in place for this community.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Analysts are often keen to build complex logic with data visualisation tools (<\/span><span data-contrast=\"none\">e.g.<\/span><span data-contrast=\"none\">\u00a0PowerBI). Packing excessive data processing into the visualisation layer can soon become a maintenance and configuration headache if left unchecked.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">One solution is to give the analysts their own project analytics &#8216;sandpit&#8217; to build new reports, test different datasets, and ensure any changes\u00a0<\/span><span data-contrast=\"none\">don&#8217;t<\/span><span data-contrast=\"none\">\u00a0negatively impact the wider production environment. You must also provide awareness training and possibly additional tooling to ensure that the relevant policies are understood and followed.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Where data is &#8216;blended&#8217; or processed from non-standard sources, you need to set clear guidelines. This exercise would include activities such as tagging, or flagging, your reporting analytics so that it identifies any data that comes from an approved source as trusted, versus any &#8216;sandpit&#8217; reports that may lack assurances of trusted provenance and data quality.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Finally, you will need to consider how to provide support and help to this community. Again, this is less a technical headache and more a cultural requirement. Solutions here will include forming various communities or forums to help spread the knowledge amongst the internal analyst workers.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Use Cases for Partners and Advanced Analysts:\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">By now, you will have covered off 80% of your project analytics use cases, but some people will want more and need to go to the raw data source. There is enormous value to be had when sharing your data with partners along the data supply chain.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">It is certainly worth highlighting the\u00a0<\/span><a href=\"https:\/\/pdataskforce.com\/about-us\"><span><span data-contrast=\"none\">Project Data Analytics Task Force<\/span><\/span><\/a><span data-contrast=\"none\">, which has made considerable progress in formulating ideas and approaches around increased data sharing for project analytics.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Ideally, it makes sense to start with a limited use case of certain suppliers providing data, then extend your partner model for data ingestion\/sharing over time. The merits of bilateral information sharing are clear for all to see, but you must ensure the appropriate governance and controls are in place from the outset.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Use Cases for Machine Learning and Artificial Intelligence:\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">There is no doubt that the potential for leveraging machine learning and AI is enormous, but you need to start with a substantial pool of quality data to train your AI\/ML models.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Even if your organisation runs many projects, for effective machine learning, that is likely to be too sparse a data set compared to other industries where models are trained over thousands or millions of data points.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">You can drop down into sub-components to gather more data, for example, by taking more granular time-slices such as monthly reports.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Whatever approach you take, your models need to run on large data sets to create statistically relevant findings.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Another aspect to consider is the &#8216;black box&#8217; nature of machine learning versus something that is more transparent and helps the users clearly understand how the model derived a result. Generally, we would recommend opting for a more transparent model to increase confidence.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Machine learning does allow you to gauge the likely outcome of a project based on learning about past performance across various feature categories. For example, you can find correlations between how well a project is run based on the quality of information entered and the project outcome.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">It makes sense to exploit external data to help train more accurate machine learning models. If you&#8217;re in the business of building bridges for example, you may only deliver a modest number of projects each\u00a0<\/span><span data-contrast=\"none\">year<\/span><span data-contrast=\"none\">\u00a0so you&#8217;ll still need a large enough sample size of bridge-building project data to create an accurate machine learning model.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Whatever data you source for machine learning, you will still need to invest in the appropriate skills and technology to execute correctly and deliver impactful use cases.\u00a0<\/span><span data-contrast=\"none\">Standard, off-the-shelf visualisation tools,<\/span><span data-contrast=\"none\">\u00a0are unlikely to be sufficient for machine learning, so you may need to invest in tools such as Databricks.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Additional Use Cases:\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">One side benefit of all this project data is that you effectively construct a Digital Twin of your operation. You can observe precisely what is happening across the entire project lifecycle. When you have project snapshot data, you can analyse the project over time, creating a powerful resource to improve the business.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">You can start to see how often people are updating the project data, altering the project baseline, and maintaining an accurate commentary of project updates.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Be careful not to leap in with assertions too\u00a0<\/span><span data-contrast=\"none\">soon, but<\/span><span data-contrast=\"none\">\u00a0gathering the right type of information will be extremely helpful for improving data quality through monitoring and improving confidence in project assurance and integrity.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Creating high-quality historical project data analytics has proven to add value far beyond machine learning and AI. As discussed, the benefits of building the Digital Twin of a major project can be far more valuable than the allure of AI and machine learning.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>Managing the Culture Change of Project Analytics:\u202f\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">Just because\u00a0<\/span><span data-contrast=\"none\">you&#8217;ve<\/span><span data-contrast=\"none\">\u00a0invested heavily in the technology and tools of project analytics doesn&#8217;t necessarily mean people will share the same passion or even use them.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Project analytics is far more than a &#8216;tech project&#8217;; you need to get people using the tools by highlighting how they positively impact their working lives and deliver better outcomes for all concerned.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">It would help if you increased the workforce and management maturity and resilience, particularly in relation to coping with the inevitable discovery of issues and problems that\u00a0<\/span><span data-contrast=\"none\">shine a light on poor performance or other &#8216;skeletons in the closet&#8217;. There will always be bumps along the way, so try and avoid any adversarial scenarios where each party is trying to prove the other wrong.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Finally, you want to instil a culture that becomes progressively less tolerant towards those who refuse to use the approved tools and revert to doing things &#8216;their way&#8217;.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">You will need to go out of your way to validate your findings and demonstrate that the information your project analytics capability provides is trustworthy and defensible. Expect some manual hard work in the near term to provide transparency, and\u00a0<\/span><span data-contrast=\"none\">don&#8217;t<\/span><span data-contrast=\"none\">\u00a0underestimate the desire for everyone to go back to their trusty spreadsheets.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">In short, culture change takes time, planning and persistence.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>The Journey Never Ends\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">We started this series in the &#8216;foothills&#8217; of project analytics and climbed steadily through the various peaks of expertise required to deliver a fully operational capability.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">The reality is that even at this late stage,\u00a0<\/span><span data-contrast=\"none\">you&#8217;re<\/span><span data-contrast=\"none\">\u00a0still at the beginning of a long journey.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">What comes next will first be impacted by changes in scope around:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Business focus<\/span><\/b><span data-contrast=\"none\">: Are you going to start reporting on additional data and other parts of the business?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Data sources<\/span><\/b><span data-contrast=\"none\">: Will there be more data sources, or more data from the same sources?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" aria-setsize=\"-1\" data-aria-posinset=\"3\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Periodicity<\/span><\/b><span data-contrast=\"none\">: Are you going to increase the frequency of reporting?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" aria-setsize=\"-1\" data-aria-posinset=\"4\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Snapshotting<\/span><\/b><span data-contrast=\"none\">: Do you want to increase snapshots that allow for more granular performance analysis over time?<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">There are plenty of factors and dimensions that influence where you go next, and of course, as your approach changes and matures,\u00a0<\/span><span data-contrast=\"none\">you&#8217;ll<\/span><span data-contrast=\"none\">\u00a0find that you&#8217;ve constructed a highly competent team that is capable of adapting.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">By this point,\u00a0<\/span><span data-contrast=\"none\">you&#8217;ll<\/span><span data-contrast=\"none\">\u00a0have:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Architected a\u00a0<\/span><span data-contrast=\"none\">solution<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"none\">Engineered the\u00a0<\/span><span data-contrast=\"none\">data<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" aria-setsize=\"-1\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"none\">Created a team of\u00a0<\/span><span data-contrast=\"none\">analysts<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" aria-setsize=\"-1\" data-aria-posinset=\"4\" data-aria-level=\"1\"><span data-contrast=\"none\">Built machine learning\/AI\u00a0<\/span><span data-contrast=\"none\">models<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"4\" aria-setsize=\"-1\" data-aria-posinset=\"5\" data-aria-level=\"1\"><span data-contrast=\"none\">Managed effective change<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">Your project community will have become far more aware of project analytics, many of them having undergone new skills training.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">You will also have created more intelligent customers, so now may be the time to invest in additional functionality because you will be better informed on the options and benefits available.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong>What Next?\u00a0<\/strong><\/p>\n<p><span data-contrast=\"none\">There are no silver bullets or &#8216;plug and play&#8217; solutions for project analytics because every organisation is unique.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">However, the building blocks exist. They have been proven and are relatively straightforward to build out.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">As\u00a0<\/span><span data-contrast=\"none\">we&#8217;ve<\/span><span data-contrast=\"none\">\u00a0highlighted in this series, the solution requires a mix of technology, business expertise and user engagement, but it is actionable and within reach.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">The key is to start small, and the rest of your journey will write itself.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this final article of the Project Analytics series, now that\u00a0we\u2019ve\u00a0covered how to launch your Project Analytics capability, we explore what each category of users will make of their newly created capabilities now the skills, processes and technology are finally in place.\u00a0 There are typically four basic use cases that we observe when building out&#8230;<\/p>\n","protected":false},"author":3,"featured_media":5408,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[404],"tags":[6,289,290,174,257,258],"class_list":["post-5407","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics-insights","tag-data-analytics","tag-data-analytics-use-cases","tag-data-consultancies","tag-data-consultancy","tag-project-data","tag-project-data-analytics"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Use Case For Project Analytics | Oakland<\/title>\n<meta name=\"description\" content=\"Once you have launched your Project Analytics capability, what will users will make of their newly created capabilities?\u00a0\" \/>\n<meta name=\"robots\" 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