How To Plan Your Innovation Journey ? Vision To Implementation In One PIcture

I received queries in response to my earlier post Building The Innovation Culture. So here is a diagram on the go to explain the steps an organization needs to follow to start its innovation journey.

Innovation Journey - Big Picture

Company's Innovation Vision has to be brainstormed, defined & documented (in that order) by the organizations leadership team. When a enterprise invests in Innovation it is critical that the Innovation should deliver some tangible benefits to the enterprise to justify next round of internal-investments. A great innovation without any purpose or an innovation which does not have tangible benefits for the enterprise is a dead investment and may lead to blockage of future Innovation funding. That is why the innovation journey of an enterprise should starts by answering the following key questions. Why , What, Who & How should help you define the Innovation Vision for your organization.

Why? 

• Organization's leadership should answer why should their organization invest in innovation?
• Each leader brings his list of points to support of the case for investment in innovation
• The leadership team brain storms Enterprise's Need For Innovation
• Leadership agrees on 'Why Innovation'

What?

• What should be the areas of focus for innovation?
• What innovations will help business growth, speed to market & value added products?
• What innovation will give the company competitive advantage?
• What innovations will help the organization build disruptive products/solutions? 
• Leadership agrees on Innovation Landscape.

Who?

• Which groups & teams need to be part of innovation journey?
• Not everybody in the organization can work on innovation      
• How will we identify people who will invest in innovation?
• How will the people contribute to innovation?

How?

• How will organization provide groups/employee time & resources to invest in innovation?
• Who and how will we monitor the investment in innovation work?
• How should we set goals & targets for 'Quick Win' innovation?
• How do we decide when to 'Pull the plus' from any innovation initiative?
• How do we recognize & reward innovation?

Once the leadership has answered the above questions they can come out with Innovation vision.
Innovation Vision helps us define the Innovation road-map and the plan to implement the innovation road-map along with key milestones. Once the implementation starts it is critical to monitor innovation investment and ensure adequate support to individuals working on innovation initiatives. The should guidelines clearly define the milestones and  innovation investment has to be reviewed at each milestone. Regular milestone reviews help the organization to monitor the progress and take a decision on continuing the investment or scrapping it.

Recommended Reading - 10 Big Data Visualization Tools Everyone in the Industry Should Be Using

Data Visualization tools like Tableau (my personal favorite) and Qlikview have been a game changer. Today the CEO/CIO/CTO are using these tools to show 'Real Time Data points' in board room meeting, using the tool to slice and dice the data, view historical data graphically and everything else you can/cannot imagine that can be done with a Visualization Dashboard.

If you have never heard of these tools here is a quick read Big Data Data Visualization at  promptcloud.com.

How Can Mobile Device Data,Tower Data,Call Data Records & Predictive Analysis Help Prevent Terror Attacks?

For most of us, our cell phones are our lifelines. Our cell phone witness and capture a lot of information about what we do, see, and share in our daily lives. We may not note or recall where all we have traveled and which route we took through the but a cell phone data can give accurate log of your activities to the second. Cell phones are recording your activities every second and sending it to telephone company, app company and Google, As I have mentioned earlier as well, a small set of data by itself seldom has any value but by data analysis and correlation with other data sets we can tell a lot about people. There are these products called Predictive Analytics tools that can this data to predict your next move. Today criminals & terrorist use this mobile phones and we can cell phones and use CDR & tower data to catch the terrorist. (Just read my blog on how Google collects cell phone data including your voice sample to know what we are dealing with today, but that's not related to today's post)

For example the terrorist who did recent kidnapping and murder of security personal in J& K can we caught using their cell phone data and future kidnapping can be prevented by using predictive analysis of this data. You might think that your cell phone is safe so long as you keep it tucked away in your pocket and do not use applications, but companies have been developing technologies to “force” our phones into giving away information without physically accessing our device. These technologies are called "Cell Site Simulators"or IMSI catchers. One of such popular products is called “Stingray.” The names may change, but all of these devices are regularly being used by USA & UK for law enforcement surveillance, These devices are designed to interfere with cell phone signals by pretending to be cell phone towers. By mimicking towers, the devices intercept signals to gather data, such as metadata and content of phone-calls, personally identifying information, and data usage, and have been especially popular as a tool for tracking the location of particular cell phones.

Technology Architecture for Mobile Surveillance

Let's assume a criminal 'A' has been involved in 10 terror incidents and another criminal 'B' was involved in 2 terror incidents they may be carrying 1 or 2 mobile phone. Predictive analysis can scan through huge bulk of location data and highlight the phone numbers that were present in the locality at the given time. We can also trace their activity for a duration of time and create a 

profile of the person and predict

1) What is his residential area

2) What are the addresses he frequents

3) What are the number of his associates and by correlation who are his associates?

4) What is a persons daily routine?

5) Does he own a personal vehicle or not

6) By further correlation with data from financial institutions we can predict the persons credit-card or banking details

7) We can tell about his family members or the people who stay with him 

As you can see this data analysis tells a lot about people that can help safely identify a persons link with crime incidents.  Security agencies can monitor their activities of shortlisted candidates and trap them to prevent crime. Apart from using the data and tools that I have mentioned Mobile Data activity surveillance requires coordination between telecom companies and government security agencies and USA/UK have been successfully able to build such mechanism and prevent crime.

Drawback - Misuse of telecom data  & prevention 

Increasingly broad use of cell site simulators by law enforcement is also controversial for many reasons. Apart from the fact that the devices themselves indiscriminately invade the privacy of everyone because they connect to and can capture data from, all cell phones within their range, the fact is these devices can also been used in controversial ways for example by deploying them disproportionately in areas made up predominantly of people of color. But this is not a serious threat and a democratic government can define protocol and monitor the use of these devices and captured data by its officials. What is important is that such monitoring devices along with "Smart Software" can monitor cell phones and at the same time secure the captured data thus protecting the privacy of non-criminal elements by way of negating cell profiles that do not have any suspicious activities over a time. Right now the priority of government is to minimize causality in areas affected by terrorism and it is critical to use Cellular Data & Data Intelligence tools to aid our security agencies.

How to upgrade R version without losing your existing installed packages

R is a language & environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. Currently it is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories.

Here are the steps I performed to reuse the downloaded libraries (saves the paint o reinstall each library) when I upgraded R from 3.5.0 to 3.5.1.

Before any update of R, start the software or Rstudio to know where all packages are installed by typing - .libPaths()

1. Before you upgrade, build a temp file with all of your old packages.

tmp <- installed.packages()
installedpkgs <- as.vector(tmp[is.na(tmp[,"Priority"]), 1])
save(installedpkgs, file="installed_old.rda")

2. Install the new version of R ( as of Oct 2018 latest version is R3.5.1)
3. Once you’ve got the new version up and running, reload the saved packages and re-install them from CRAN.

tmp <- installed.packages()
installedpkgs.new <- as.vector(tmp[is.na(tmp[,"Priority"]), 1])
missing <- setdiff(installedpkgs, installedpkgs.new)
install.packages(missing)
update.packages()