I found Salesforce Timothy McCormick’s blog post on the citizen engagement initiatives within the City of Philadelphia, including the launch of the new 311 non emergency Customer Relationship Management (CRM) software, was on point as it pertains to the growth of excellence citizen engagement and experience in the public sector.
Below is an excerpt from the blog and link to the entire blog post for your review.
Citizen engagement is less than desirable–with long lines, lots of paperwork, and the confusion of a bureaucracy make it hard for citizens to access the right information. How often are citizens reporting issues vs. commenting (or complaining) on a soap box over social? How many elected positions ran with uncontested candidates in your last election?
Timely responses. How many times have you thought, “What more can we do to make this move faster? Why does progress on XYZ project seem to move so slow compared to everything else in life? How can we possibly do more with stricter budget and fewer resources all around?” Not only does this make it hard to motivate teams, but also it causes citizens to lose faith as they see responses lag and vague delivery commitments, impacting the government’s respectability from the perspective of their customers.
Transparency is difficult to deliver. Without transparency into the decision making process, progress against a request, or delivery impactors, citizens are left to make assumptions, that when paired with a lack of trust, tend to have a negative impact on relations with their governing bodies. Do you feel like this has impacted citizen relationships with your organization(s), such as relations with local politicians, or the police department?
So why all of the sudden are these pain points more prevalent? Why is citizen engagement stagnant, or in some cases dropping? Why does the gap between timely delivery and citizen expectation seem to be growing, no matter what? Why is providing transparency so much more difficult today?
The answer is easy: impact of technology trends and transformation. Here are some trends to consider:
Mobile gives citizens the power to connect to their government anywhere, anytime–and they have come to expect that level of engagement now that mobile is commonplace. This is good for government, as always-on citizens give organizations the ability to collect more data in context, enabling leaders to prioritize with more accuracy and be more aligned with what citizens care about all around.
Anywhere, anytime citizens tend to be anytime, anywhere customers. This means they have come to expect social interfaces as the user interface as much as they expect mobile accessibility, giving them an always-on receptacle for comments, inquiries, and request status. Social Platforms help governments meet these demands in a scalable, cost conscious way by supplying a transparent and collaborative platform for engagement that is friendly to Q&A at the pace of conversation.
With technology expanding an organization’s potential reach, apps are becoming more and more popular as an internal asset. They are easily adapted to the next big mobile or tech trend (think apps for the Apple watch), helping organizations modernize/rationalize dated infrastructure at the pace of their citizens.
More and more devices are coming online, revealing data that could never before be captured. While many organizations we talk to see this as a daunting, overwhelming force to be reckoned with, it’s not! By connecting ordinary objects, such as busses, trains, or stoplights to the internet, (made easier to service with apps on a common platform!) citizens will start to expose behavioral patterns that…
Unlock all kinds of data never before detectable. With increased data availability, variety, and context around everyday activities and citizen behavior patterns, officials can better inform government strategy and resource planning. If you are interested in learning more about how to apply and benefit from a data strategy, join us for Philly Innovates. Mayor Michael Nutter and his team are hosting the first-ever Innovation Summit live in the city, and will share how they addressed these tech trends to realize bottom-line benefits.
Customer experience–and therefore citizen experience–is the new differentiator, as new technologies enable customized, personal, more meaningful experiences with a given organization. Just look at how taxi services have morphed so quickly with companies like Lyft and Uber breaking down barriers between private and public sectors, changing the competitive landscape like government has never before seen. There is no reason why agencies can’t take this same approach to citizen services.
Click here to read the entire blog post: http://blogs.salesforce.com/company/2015/01/6-trends-shaping-governmentcitizen-relationships-.html
I wanted to reblog this post by Tony Consentino, Ventana Research VP and Research Director, because it was very insightful and thought provoking. In summary, when using or talking about big data, one should think of terms “What, So what, Now what & Then what”.
Read originally post by clicking this link: Process Trumps Innovation in Business Analytics
The idea of not focusing on innovation is heretical in today’s business culture and media. Yet a recent article in The New Yorker suggests that today’s society and organizations focus too much on innovation and technology. The same may be true for technology in business organizations. Our research provides evidence for my claim.
My analysis on our benchmark research into information optimization shows that organizations perform better in technology and information than in the people and process dimensions. They face a flood of information that continues to increase in volume and frequency and must use technology to manage and analyze it in the hope of improving their decision-making and competitiveness. It is understandable that many see this as foremost an IT issue. But proficiency in use of technology and even statistical knowledge are not the only capabilities needed to optimize an organization’s use of information and analytics. They also need a framework that complements the usual analytical modeling to ensure that analytics are used correctly and deliver the desired results. Without a process for getting to the right question, users can go off in the wrong direction, producing results that cannot solve the problem.
In terms of business analytics strategy, getting to the right question is a matter of defining goals and terms; when this is done properly, the “noise” of differing meanings is reduced and people can work together efficiently. As we all know, many terms, especially new ones, mean different things to different people, and this can be an impediment to teamwork and achieving of business goals. Our research into big data analytics shows a significant gap in understanding here: Fewer than half of organizations have internal agreement on what big data analytics is. This lack of agreement is a barrier to building a strong analytic process. The best practice is to take time to discover what people really want to know; describing something in detail ensures that everyone is on the same page. Strategic listening is a critical skill, and done right it enables analysts to identify, craft and focus the questions that the organization needs answered through the analytic process.
To develop an effective process and create an adaptive mindset, organizations should instill a Bayesian sensibility. Bayesian analysis, also called posterior probability analysis, starts with assuming an end probability and works backward to determine prior probabilities. In a practical sense, it’s about updating a hypothesis when given new information; it’s about taking all available information and finding where it converges. This is a flexible approach in which beliefs are updated as new information is presented; it values both data and intuition. This mindset also instills strategic listening into the team and into the organization.
For business analytics, the more you know about the category you’re dealing with, the easier it is to separate what is valuable information and hypothesis from what is not. Category knowledge allows you to look at the data from a different perspective and add complex existing knowledge. This in and of itself is a Bayesian approach, and it allows the analyst to iteratively take the investigation in the right direction. This is not to say that intuition should be the analytic starting point. Data is the starting point, but a hypothesis is needed to make sense of the data. Physicist Enrico Fermi pointed out that measurement is the reduction of uncertainty. Analysts should start with a hypothesis and try to disprove it rather than to prove it. From there, iteration is needed to come as close to the truth as possible. Starting with a gut feel and trying to prove it is the wrong approach. The results are rarely surprising and the analysis is likely to add nothing new. Let the data guide the analysis rather than allowing predetermined beliefs to guide the analysis. Technological innovations in exploratory analytics and machine learning support this idea and encourage a data-driven approach.
Bayesian analysis has had a great impact not only on statistics and market insights in recent years, but it has impacted how we view important historical events as well. It is consistent with modern thinking in the fields of technology and machine learning, as well as behavioral economics. For those interested in how the Bayesian philosophy is taking hold in many different disciplines, I recommend a book entitled The Theory That Would Not Die by Sharon Bertsch McGrayne.
A good analytic process, however, needs more than a sensibility for how to derive and think about questions; it needs a tangible method to address the questions and derive business value from the answers. The method I propose can be framed in four steps: what, so what, now what and then what. Moving beyond the “what” (i.e., measurement and data) to the “so what” (i.e., insights) should be a goal of any analysis, yet many organizations are still turning out analysis that does nothing more than state the facts. Maybe 54 percent of people in a study prefer white houses, but why does anyone care? Analysis must move beyond mere findings to answer critical business questions and provide informed insights, implications and ideally full recommendations. That said, if organizations cannot get the instrumentation and the data right, findings and recommendations are subject to scrutiny.
The analytics professional should make sure that the findings, implications and recommendations of the analysis are heard by strategic and operational decision-makers. This is the “now what” step and includes business planning and implementation decisions that are driven by the analytic insights. If those insights do not lead to decision-making or action, the analytic effort has no value. There are a number of things that the analyst can do to make the information heard. A compelling story line that incorporates storytelling techniques, animation and dynamic presentation is a good start. Depending on the size of the initiative, professional videography, implementation of learning systems and change management tools also may be used.
The “then what” represents a closed-loop process in which insights and new data are fed back into the organization’s operational systems. This can be from the perspective of institutional knowledge and learning in the usual human sense which is an imperative in organizations. Our benchmark research into big data and business analytics shows a need for this: Skills and training are substantial obstacles to using big data (for 79%) and analytics (77%) in organizations. This process is similar to machine learning. That is, as new information is brought into the organization, the organization as a whole learns and adapts to current business conditions. This is the goal of the closed-loop analytic process.
Our business technology innovation research finds analytics in the top three priorities in three out of four (74%) organizations; collaboration is a top-three priority in 59 percent. Both analytics and collaboration have a process orientation that uses technology as an enabler of the process. The sooner organizations implement a process framework, the sooner they can achieve success in their analytic efforts. To implement a successful framework such as the one described above, organizations must realize that innovation is not the top priority; rather they need the ability to use innovation to support an adaptable analytic process. The benefits will be wide-ranging, including better understanding of objectives, more targeted analysis, analytical depth and analytical initiatives that have a real impact on decision-making.
VP and Research Director