As someone once said, the problem with Big Data is that it's... well, BIG. Long before the term exploded into the vocabulary of the general public in mid-2011, industries everywhere have delighted in the fact that Big Data can provide them with much-needed insight. By looking for Big Data both internally and externally, organizations can gain the intelligence needed to become more responsive to customer needs, stand out from their competition, and ultimately, be more profitable. Here's a quick look at the four qualities of Big Data that make it a unique tool for generating actionable business insight: Volume: Why Big Data is Big Everyday, people are spending huge chunks of time working and socializing in the digital world, which generates an enormous amount of data. According to a 2014 infographic by DOMO, every minute there are 277,000 tweets posted to Twitter, 216,000 photos uploaded to Instagram, and 8,333 videos shared on Vine. Now imagine being able to tap into all of that social activity to identify how consumers are finding your website online, or how much time they spend using your SaaS tool on a weekday. That's what Big Data is about — allowing users to access this mind-boggling volume of information and then process it in order to pinpoint actionable items. Variety: Handling Structured and Unstructured Data Generally, structured data is information that's highly organized and easy to search using straightforward search engine algorithms. A concrete example would be spreadsheets — information is presented in columns and rows, making it easy to search and sort. Unstructured data, on the other hand, is the opposite. It usually consists of human-generated and people-oriented content that may not fit neatly into database tables. The best example of unstructured data: email. Let's face it, information in email is chaotic. If you had to force all the data in your inbox into the grids of a spreadsheet, you'd soon understand the problem. And yet, Big Data can do so much more using both structured and unstructured data. There are newer, better machine learning algorithms that can differentiate signal from noise. Look at Google Flu Trends, which is able to predict the number of flu cases in a certain country based on search data surrounding keywords such as "flu," as opposed to relying directly on health reports. It's one example of how Big Data can parse even unstructured data (e.g. web searches), resulting in a useful tool. Velocity: Delivering Insights in a Snap Moore's Law states that the overall processing power of computers doubles every two years. As technology's capacity grows exponentially, it allows Big Data to deliver insight to its users at a greater velocity. Data is generated in real time, so users can demand actionable information in real time. A decade ago, this would have been impossible. But today, it's a reality that is opening new doors. Value: The Goal is Intelligence All this comes back to the fact no organization collects data just for the sake of having data. They're doing it to derive actionable insight. This is the value you want to extract. And it will only happen if all three previous V's are addressed in equal measure: volume, variety, and velocity. How has Big Data helped you and your organization? Hit the comments and tell us.
What if there was a reporting option as powerful as a BI tool but more accessible to business users and already built into your collaborative work management software? Meet Wrike Analyze: advanced reports and analytics easily accessible within Wrike. Read to learn more of its features!
You created a perfect project plan but then... one of your tasks took longer than expected, a new assignment came out of the blue, and one of the key team members took several days off for a sick child. Don't let unexpected events frustrate you: the brand-new Critical Path analysis view addresses all these issues. Now when something gets in your team's way, you can instantly see what project schedule alterations can be made without delaying milestones or missing deadlines. This new Enterprise feature, which was hotly requested by users, displays the longest chain of dependent tasks in your project folder on the Timeline view. This view allows you to focus on the vital string of events that must be accomplished to stay on track. Once you review your critical path, you can regroup your resources to get those main tasks done, and reschedule nonessential work that is getting in the way of hitting your project milestone date on time. Now that Wrike possesses Critical Path, it should make the migration from MS Project even easier. Critical path analysis is a project management method that creates the shortest timeline for your project by considering the tasks required to complete the deliverable, along with time estimates for each step, task dependencies, and final milestones. This methodology helps you intelligently plan every project. You may be wondering how to calculate critical path for your projects. To use our new feature, go to your Timeline view and tick the "Critical Path" radio button at the top of your workspace. This feature is available for Enterprise accounts. Now that it's live in your workspace, we'd love to get your feedback on it. Hit the comments and tell us what you think!
Our friends over at TechnologyAdvice spend a lot of time gathering the best tech advice for you to improve your work performance. This guest post from one of their writers, Christopher Herbert, tells you the three things you need to take your projects down the road to success. A project’s success or failure is judged by its adherence to the guidelines established when it began. This is both typical and reasonable, but only if those guidelines are well constructed and attainable. Often, an executive declares a project a failure based on the failure to operate within the allotted budget, failure to complete on time, or failure to produce a product of high enough quality. But such a judgment can be complicated. A project within its budget but failing to meet all the stakeholder’s ever-shifting needs may be considered a failure, while an ambitious project may go way over budget but still be considered a success because at least it was properly completed. These subjective definitions of failure reveal that the measurement of a project’s success is not always about delivery, but about perception. If the project guidelines can be better defined before it starts, it has a greater likelihood of achieving success. Data can improve these expectations. Data about past projects can help you set appropriate, attainable goals, and inform you as to how much of the unexpected should actually be expected. 1. Planned Versus Actual Start/End Dates for Similar Projects This data point requires that you’ve performed projects of this kind before. Simply collecting the actual start dates and actual end dates of previous projects (not the “projected” dates) will give you an average time-to-completion estimate, as well as high and low possibilities. For further predictive accuracy, find the number of work days between those two dates, accounting for holidays, corporate training days, employee PTO vacation days, and unexpected sick days. Then find the number of problems faced, how long it took to address them, the number of adjustments made to the original plan, and time lost due to plan adjustments. Much of this data may not be included in the official report, so it may require digging through emails, records in your project management tool, meeting minutes, or questioning those involved. Use this data to compare with the holidays, corporate training, PTO days, etc. that are planned to occur within the timespan of your upcoming project. Throw in the average unexpected setbacks that occurred in each previous project. This estimate, being based on previous experiences, has a higher likelihood of accuracy than a gut instinct, or even average project time. It may also be more convincing when attempting to explain why a project will simply take longer to complete than the executives would like. 2. Project Team Productivity Data This data point does not require prior projects of its kind per se, but it does require that you have some sort of data on individual employee productivity for similar tasks to those required by the current project. This requires a certain degree of willingness from your staff to either allow the monitoring - or participate in the monitoring - of how long it takes them to accomplish certain tasks. Ideally, this would be more than just a generalized, “It takes me two hours to do this.” Rather, it needs to be consistent, non-intrusive observations of the rate of task completion (such as noting each time an item is completed). Whether its measured in words per minute, lines of code per hour, or pots painted per day, this data can be used to create average productivity estimates, as well as minimum and maximum productivity levels. Once you’ve identified a realistic rate of production, you can either use that information to calculate team progress manually or with the help of project management software. Many PM applications allow you to assign tasks to a team of workers. With productivity data for each team member already entered, the software will give you an estimated completion time for the entire project. This allows you to estimate time-to-completion of a team’s assignments based on of the cumulative productivity of each employee, with data to back it up. One thing to be wary of is that many employees are uncomfortable with particularly detailed monitoring of their task progress. Assure them that the monitoring is purely for the sake of setting reasonable goals on future projects, which in turn will lead to higher success rates, and fewer project cancellations. 3. Resources and Materials Consumption Data Material consumption is some of the easiest data to find accurate values for, and can save you serious money. Analyzing the relationship between the amount of materials anticipated, requested, used, and leftover from previous projects should help you better calculate the amount required for your upcoming project. Similarly, quality assurance data on the frequency of defective products will help you anticipate the cost and delay of making up for imperfections. Aside from the materials alone, entire budgets of prior projects can be broken down and analyzed to find which areas frequently overspend, and therefore which areas need to be better planned for. Whether that be a particular resource, labor, or tool, it’s likely to go over budget again, unless real changes were made. All of this data collection is purely for the purpose of improving your project’s expectations beforehand. It’s the difference between simply picturing a finished bridge or piece of software in three months, and actually calculating out the time it will take for each team to perform their tasks based on numbers from their previous performances. Which method would you use to determine a project’s eventual success or failure?