The Data Culture Checklist
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The Data Culture Checklist Whether you’re just starting your analytics journey or ready to take an established data culture to the next level, here are tips and advice to help you at every stage. Why is a data culture vital? Your business already generates And just as a set of blocks needs mountains of data by doing what an imaginative child to build it does every day. You most likely them into a house, a car, a But what are the capture and process a fraction bridge, your data also need a of this data in the form of vision and a plan to transform numbers really telling transaction records, website them into meaningful insights. analytics, even P&L Only then can your data you? What other spreadsheets. intelligence gain the necessary authority and trust to inform valuable intelligence But what are the numbers really decisions and guide the activities could be hiding in the telling you? What other valuable of your team. intelligence could be hiding in the rest of that raw data? rest of that raw data? And, This checklist sets out how to looking at the same reports, create and nurture an active would everyone in your team data culture with basic, reach the same conclusions? intermediate and advanced tactics. Data are neutral. Numbers aren’t insights. They’re only the building blocks to business intelligence. What the Data Says: 44% 18% … of companies believe their data is … of PR, marketing and business trapped in silos, inaccessible to those professionals rate themselves as who need it. having a high level of data literacy. Basic Basic 2. Identify the metrics you will use The most easily accessed metrics may not reveal what you really need to know, mainly when collated from third-party providers and platforms. Check how each source defines the metric to see if it matches the intent of your KPI. For example, YouTube counts a ‘View’ whenever someone watches for 30 seconds or more – whether the video 1. Document your data strategy runs for 31 seconds or five minutes. Unsurprisingly, any journey should begin with a map. Start by agreeing and Meanwhile, Facebook counts a view after documenting your existing KPIs with the only three seconds. Using either metric – or rest of the team, so everyone understands worse, collating or comparing both – could what is to be measured – and why. Clearly give a false impression of how successful a define each KPI as unambiguously as video is. A better KPI might be the number possible to reduce the risk of disputes or who viewed 90-100% of the video, requiring confusion later. you to dig deeper into the analytics. Basic 1 2 3 4 5 Basic 3. Clean your data Many businesses only discover how 4. Maintain data integrity inconsistent, incomplete and inaccurate their data is when implementing a new data Once the data is cleaned and consistent, strategy, meaning any findings will be establish clear data entry practices and misleading, skewed or completely useless. occasional checks to prevent it gradually And if your team learns to distrust the data, reverting to its previous chaotic state. It any decisions made as a result will also be may be possible to reduce human error by distrusted – or ignored. Don’t be using system plug-ins or APIs to disheartened! Cleaning the database(s) automatically transfer relevant data from can be a formidable task (professional one platform or database to another. For services are available), but any short-term processes that still require human data pain will soon be rewarded when you begin entry, create quick and easy processes – turning that raw data into valuable such as dropdown lists – with as few intelligence. required fields as possible. Basic 1 2 3 4 5 Basic “Tell me how you measure me, and I will tell you how I behave.” 5. Share the data behind all decisions When communicating decisions across the business, briefly summarise how the data and insights helped form the conclusions. This not only demonstrates data-led decision-making by example but also increases the team’s understanding of broader business performance. - E. M. Goldratt, The Haystack Syndrome. Basic 1 2 3 4 5 Intermediate Intermediate 1. Invest in data literacy for every 2. Audit your KPIs and metrics for team member negative consequences. Analyzing and interpreting data takes both Volume metrics, in particular, can have a mathematical understanding (does unintended side-effects, skewing activity everyone know the difference between towards whatever is being measured at the average and median, for example?) as well expense of other more qualifying factors. as some business acumen. Only then can This can undermine the positive intent of enough context be applied to the numbers your KPIs. and graphs to suggest what they might mean, test various hypotheses and inform For example, if the volume of clicks future decisions. determines content effectiveness, producers may increasingly adopt You don’t need your team to become data “click-bait” tactics to meet the KPI, eroding scientists. However, a moderate degree of the content’s real purpose. While this might data literacy not only helps each team seem like gaming the system, they’re merely member become more comfortable working responding to the set KPI. Review your KPIs with data but also makes it easier for them and metrics after a reasonable period to to see how their performance contributes see how individual behaviors may have towards each KPI. adjusted in response. Intermediate 1 2 3 4 Intermediate 4. Interrogate and corroborate your data 3. Visualize the data to tell a story Even if you follow all the above, your data How you present and communicate data may still suggest an insight or conclusion intelligence to the team, and the business that seems wrong or misleading. Resist the as a whole, is a skill in itself. A clinical series temptation to overrule a finding just of graphs, percentages, and numbers can because your gut disagrees and instead oversimplify what’s happening by leaving test the results. Check if other metrics out relevant context, history, and meaning, confirm, clarify or contradict the findings – relying on the people reading the report to such as reconciling sales numbers with fill in the gaps and apply their stock levels. interpretations. Things that aren’t alike may be compared as if they are, leading to Look for discrepancies that may have crept misleading conclusions and bad decisions. into the data over time. Perhaps the Create a reporting format that allows you methods used to compile the data need to structure and visualize your findings with revising to account for new factors skewing the necessary context, background, and the results. Even if there are no red flags, an caveats, presenting a clear narrative occasional sense check can be good everyone can follow. practice to optimize and improve your data strategy. Intermediate 1 2 3 4 Advanced Advanced 1. The transition from static to dynamic data presentation Many businesses achieve a lot with the standard tools; collating and crunching data in Excel before pasting graphs into PowerPoint to present the results. But static presentations like these can limit the information you include, how each dataset is presented (bar chart or trend graph, for example) and even the linear order in which you show them. Alternatively, business intelligence and data analysis software like Tableau and Sisense offer far more flexibility. 2. Encourage data sharing across the business Users design a set of visualizations or dashboards that everyone can manipulate in Different departments will likely have distinct real-time. Let your team interrogate the data: KPIs and metrics related to their activities. filter to analyze a particular region, compare However, this can lead to specific datasets or specific segments of your audience, or adjust intelligence becoming siloed or invisible to KPI targets to view what else is affected. With others who may also find the information interactive visualizations, you’ll spend more useful – particularly when combined or time coming up with actionable compared with other datasets. Opening up recommendations and less time saying, “let access to all datasets and reports across the me get you that data.” company can lead to more valuable insights. Advanced 1 2 3 4 Advanced 3. Move from data analysis to data exploration 4. Work with a data scientist Data analysis uses established metrics and datasets to answer clearly defined problems While data analytics deals with specifics, data or questions – such as measuring a science explores more open-ended questions documented KPI or comparing sales periods. such as “How can we reduce costs.” Data However, this may only use a fraction of the scientists are particularly skilled in exploring raw data available to your business. Once your unstructured data in search of hidden value. data strategy has matured, exploring and Most businesses might not need (nor afford) experimenting these untouched datasets can to hire a full-time data scientist. Many data reveal answers to questions you would never scientists prefer contract or project-based even know to ask – hitherto unknown patterns work anyway as it allows them to go wherever that might lead to new opportunities or there might be an interesting problem to solve. trends. This is where data science takes over … Advanced 1 2 3 4 Digital communications & PR made easy To find out more about Mynewsdesk, go to Mynewsdesk.com.