Today’s CIO has more data available than ever before. There is an opportunity for potential big improvements in decision-making outcomes, it carries huge complexity and responsibility in getting it right.

Many have already got it wrong and this is largely in part down to organisational culture. At the centre of creating a successful analytics strategy is building a data-driven culture.

According to a report by Gartner more than 35% of the top 5,000 global companies will fail to make use of the insight driven from their data. In another report by Eckerson, just 36% of the respondents gave their BI program a grade of ‘Excellent’ or ’Good’.

With the wealth of data already available in the world and the promise that it will continue to grow at an exponential rate, it seems inevitable that organisations attempt to leverage this resource to its fullest to improve their decision-making capabilities.

Before we move forward, it’s important to state that underpinning the success of these steps is to ensure all employees who have a direct involvement with the data or the insight generated are able to contribute. This point is highlighted in a case study of Warby Parker who illustrate the importance of utilising self-service technologies that help all users meet their own data needs, which, according to Carl Anderson, the director of Data Science, is essential in realising a data-driven culture.

Set Realistic Goals

I suppose this step is generic and best practice across all aspects of an organisation. However, I felt it needed to be mentioned because there are a number of examples available where decision-makers have become disillusioned with their analytics program due to it not delivering what they had expected.

Therefore, CIO’s should take the time to prepare in-depth research into their organisation; I recommend they look at current and future challenges facing their organisation and tailor their analytics strategy appropriately around solving these.

During this process, it is important to have a full understanding of the data sources currently used for analysis and reporting by the organisation as well as considering the external data sources available to the organisation that are not yet utilised.

By performing extensive research and gaining understanding on the data sources available to the organisation, it will be easier for CIO’s to set realistic and clear goals that address the challenges facing the business. Though there is still work to be done addressing how the analytics strategy will go about achieving these goals, it’s at this point where CIO’s need to get creative with the data available to them.

For example, big data has brought with it a wealth of unstructured data and many analysts believe that tapping into this unstructured data is paramount to obtaining a competitive advantage in the years to come. However it appears to be something that most will not realise any time soon as according to recent studies estimate that only around 0.5% percentage of unstructured data is analysed in the world.

Build the Right Infrastructure

Once the plan has been formulated, the next step for CIO’s is to ensure that their organisation’s IT infrastructure is aligned with the strategy so that the set goals can be achieved.

There is no universal “one way works for all” solution on building the right infrastructure; the most important factor to consider is whether the IT infrastructure can work according to the devised strategy.

A key requirement and expectation underpinning all good, modern infrastructures is the capability to integrate all of the data sources in the organisation into one central repository. The benefit being that by combining all of the data sources it provides users with a fully holistic view of the entire organisation.

For example, in a data environment where all of the organisation’s data is stored in silo, analysts may identify a trend or correlation in one data source but not have the full perspective afforded if the data were unified, i.e. what can our other data sources tell us about what has contributed to this correlation?

Legacy technologies that are now obsolete should be replaced in favour of more modern approaches to processing, storing and analysing data – one example are those technologies built on search-engine technology, as cited by Gartner.

Enable Front-Line Employees and Other Business Users

Imperative to succeeding now is ensuring that front-line employees (those whose job roles can directly benefit by having access to data) and other business users (managers, key business executives, etc.) are capable of self-serving their own data needs.

CIO’s should look to acquire a solution built specifically for self-service analysis over large-volumes of data and capable of seamless integration with their IT infrastructure.

A full analysis of employee skill-set and mind-set should be undertaken to determine whether certain employees need training in particular areas to bolster their knowledge or simply need to adapt their mind-set to a more analytical one.

Whilst it is essential that the front-line employees and other business users are given access to self-service analysis, inherently they will likely be “less-technical users”. Therefore ensuring they have the right access to training and other learning tools is vital to guarantee that they don’t become frustrated or disheartened.

By investing in employee development in these areas now, it will save time and money further down the line, removing an over reliance on both internal and external IT experts.

Source: The 3 Step Guide CIO’s Need to Build a Data-Driven Culture

Originally Posted at: Using sparklyr with Microsoft R Server

[youtube https://www.youtube.com/watch?v=SB29nSaCppU]

In this podcast Jason talked about the fabric of a great transformative leadership. He shared some tactical steps that current leadership could follow to ensure their relevance and their association with transformative teams. Jason emphasized the role of team, leader and organization in create a healthy future proof culture. It is a good session for the leadership of tomorrow.

Jason’s Recommended Read:
Reset: Reformatting Your Purpose for Tomorrow’s World by Jason Brooks https://amzn.to/2rAuywh
Essentialism: The Disciplined Pursuit of Less by Greg McKeown https://amzn.to/2jOX8Xi

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Jason’s BIO:
Dr. Jason Brooks is an executive, entrepreneur, consulting and leadership psychologist, bestselling author, and speaker with over 24 years of demonstrated results in the design, implementation and evaluation of leadership and organizational development, organizational effectiveness, and human capital management solutions, He work to grow leaders and enhance workforce performance and overall individual and company success. He is a results-oriented, high-impact executive leader with experience in start-up, high-growth, and operationally mature multi-million and multi-billion dollar companies in multiple industries.

About #Podcast:
#JobsOfFuture podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ info@analyticsweek.com

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#JobsOfFuture #Leadership #Podcast #Future of #Work #Worker & #Workplace

Source: @DrJasonBrooks talked about the Fabric and Future of Leadership #JobsOfFuture #Podcast by v1shal

We all know about the promise of Big Data Analytics to transform our understanding of the world. The analysis of structured data, such as inventory, transactions, close rates, and even clicks, likes and shares is clearly valuable, but the curious fact about the immense volume of data being produced is that a vast majority of it is unstructured text. Content such as news articles, blog post, product reviews, and yes even the dreaded 140 character novella contain tremendous value, if only they could be connected to things in the real world – people, places and things. In this talk, we’ll discuss the challenges and opportunities that result when you extract entities from Big Text.

Speaker:
Gregor Stewart – Director of Product Management for Text Analytics at Basis Technology

As Director of Product Management, Mr. Stewart helps to ensure that Basis Technology’s offerings stay ahead of the curve. Previously Mr. Stewart was the CTO of a storage services startup and a strategy consultant. He holds a Masters in Natural Language Processing from the University of Edinburgh, a BA in PPE from the University of Oxford, and a Masters from the London School of Economics.

Thanks to our amazing sponsors:

MicrosoftNERD for Venue

Basis Technology for Food and Kindle Raffle

Video:

Slideshare:

Originally Posted at: Making sense of unstructured data by turning strings into things by v1shal

rxSparkConnect()
sampleHiv <- RxHiveData(table="hivesampletable")

# this will create the composite Xdf 'samplehivetable'
sampleXdf <- as_xdf(sampleHiv)

sampleXdf %>%
    filter(deviceplatform == "Android") %>%
    group_by(devicemake) %>%
    summarise(n=n()) %>%
    arrange(desc(n)) %>%
    head()
#>     devicemake     n
#> 1      Samsung 16244
#> 2           LG  7950
#> 3          HTC  2242
#> 4      Unknown  2133
#> 5     Motorola  1524

sampleHiv %>%
    filter(deviceplatform == "Android") %>%
    group_by(devicemake) %>%
    summarise(n=n()) %>%
    arrange(desc(n))
#> # Source:     lazy query [?? x 2]
#> # Database:   spark_connection
#> # Ordered by: desc(n)
#>     devicemake     n
#>           
#> 1      Samsung 16244
#> 2           LG  7950
#> 3          HTC  2242
#> 4      Unknown  2133
#> 5     Motorola  1524
#> # ... with more rows

# copy the flights dataset to SQL Server
flightsSql <- RxSqlServerData("flights", connectionString=connStr)
flightsHd <- copy_to(flightsSql, nycflights13::flights)

# this is run inside SQL Server by dplyr
flightsQry <- flightsSql %>%
    filter(month > 6) %>%
    group_by(carrier) %>%
    summarise(avg_delay=mean(arr_delay))

flightsQry
#> # Source:   lazy query [?? x 2]
#> # Database: Microsoft SQL Server
#> #   13.00.4202[dbo@DESKTOP-TBHQGUH/sqlDemoLocal]
#>   carrier avg_delay
#>          
#> 1 "9E"        5.37 
#> 2 AA        - 0.743
#> 3 AS        -16.9  
#> 4 B6          8.53 
#> 5 DL          1.55 
#> # ... with more rows

install_packages(c("dplyr", "dbplyr", "odbc", "sparklyr"),
                 repos="https://cloud.r-project.org")
devtools::install_github("RevolutionAnalytics/dplyrXdf")

Source by analyticsweekpick

Have you ever tried memorizing periodic table? It is a daunting task as it has lot of elements and all coded with 2 alphabet characters. So, what is the solution? There are various methods used to do that. For one check out Wonderful Life with the Elements: The Periodic Table Personified by Bunpei Yorifuji. In his effortm Bunpei personified all the elements. It is a fun way to identify eact element and make it easily recognizable.

In his book, Yorifuji makes the many elements seem a little more individual by illustrating each one as as an anthropomorphic cartoon character, with distinctive hairstyles and clothes to help readers tell them apart. As for example, take Nitrogens, they have mohawks because they “hate normal,” while in another example, noble gases have afros because they are “too cool” to react to extreme heat or cold. Man-made elements are depicted in robot suits, while elements used in industrial application wear business attire.

Image by Wired

Source: Periodic Table Personified [image] by v1shal