Category: Analytics

As we already know, changes in an organisation are inexorable and so does the necessity for effective change management. By change management, we refer to a systematic method which facilitates the development of employees, their teams, and thus the overall organisation from a current nascent stage to a highly advanced future stage. This organisation change management is executed to accomplish a goal or a specific strategy in addition to positively bolstering the employees to welcome these changes and effectuate them warmly.
With the introduction of changes, numerous factors, goals, and aspects come into the picture. Irrespective of changes getting affected by external elements such as political, social, or economic ones, or by the internal components such as policies and structures, the organisational change management needs to be accompanied with a fully functional strategy and vision. The approach is a consolidated and exhaustive plan which paves the path for achieving the goal of initiating changes.
While changes can be looked positively, some may find this to be highly incompatible, thus starting to disregard or fear it. In such situations, the role of a leader in laying out the leadership strategy in organisation change management becomes paramount. Being a leader, it becomes your duty to motivate and teach the aspects of changes in your subordinates. Especially when you develop excellent and lousy outlook towards the changes, keeping yourself stable and ensuring the same with the team is mandatory in an organisation.
With these points to be considered, understanding the positive and negative aspects of changes and imbibing leadership strategy in organisational change management is one of the primary elements of leadership. Understand the below-mentioned points to overcome the challenges faced in organisation change management and how to blend leadership strategy in making the organisation change management a successful one.
Leadership Strategy in Organization Change Management

• Expedite your relationship with the team –

Since you are the team leader and you have, over time, amalgamated an excellent repo with your team members, you should look out for abating any opposition expected from the employees. Try to comprehend the repercussions of the changes on the employees and resolve them at a personal level with your employees. Gather the information received from the employee feedback and keep notice of the positives and the negatives.

• Establishing effective communication –

Adroit communication between the team leader and the employees is mandatory for the effective implementation of necessary changes. If the information is passed and the colleagues do not comprehend it, it will lead to lots of confusion and resistance from everyone. Try to segregate the facts and data associated with the changes into phases for a transparent deliverance of the required information. This way, the employees will be capable of clearly understanding the aspects of these changes and study them properly.

• Ensuring staff turnover

Staff turnover herein means more amount of active participation from the employees. When a change is executed, the first important leadership strategy is to provide maximum staff participation. Engage them in the effectuation of the plans and policies associated with the changes. Encourage their active involvement in this initiative by adequately mentoring them and continually augmenting their respective roles.
For a productive organisation change management, you need to teach such leadership strategy which is flexible as well as planned out expertly. Always come up with a roadmap to achieve the laid out goals with constant engagement from the employees. Ensure that they understand the need for these changes and try to create a positive outlook of the team members towards these changes in the organisation.

Data science is ruling almost every society today. From marketing to retail, hospitality to travel, entertainment and sports along with finance and insurance, data science is everywhere. Data science has left no stone untouched to deliver what it claims and hence adds value to society. Owing to such a robust technology, several enterprises are finding it hard to cope with the same. Few beliefs it’s a threat whereas some consider it a massive success.
In the cacophony of above, there are quite a lot of people or say industries that are still not open to this technology and consider the term daunting. Many organisations are struggling to uncoil its true meaning. So, here we unbox few of the myths about data science rumoured in the digital world.
10 Most Common Misconception About Data Science

1. Data science finds its application only in humongous data: Contradicting, It’s not always necessary to have a pool of data to perform operations. Data science can very well work on Volume-based, veracity based, velocity based and variety based data.
2. The higher the data density, the more accurate the results are: People often believe in the more, the merrier. However, this does not imply to data science. The more significant amount of data calls for complexities and boosts oversights. Quantity is not proportional to quality. Exposure to a set of data would make you wander from where to start.
3. Artificial Intelligence would soon replace data scientist: The year 2012 tagged Data Scientist as one of the sexiest hops across the globe. We need Data Science to implement artificial intelligence. But considering the former to overtake or entirely replace is not feasible. Data science helps in gathering data required by AI, but the way the AI functions need the guidance of data scientist.
4. All a data scientist needs to do is learn a tool: Few of the lot considers data science to be as simple as learning an instrument. But no, data science scans beyond. Data science spawns algorithm and tools and is way beyond just learning to code. You need to dive in deeper to have an understanding of the data and work upon the statistics to shine better.
5. Data Science Do Not Yield Monetary Benefits: Data Scientist study insights of data and then strive to guide you further. Data science applied to the channels of marketing of the company helps you optimise them and then invest in the best. This is a way would boost ROI.
6. Business Intelligence and Data Science Are Same: Where Business Intelligence is all about what, when, how, who, data science scrawls across why did it happens, will it happen again? Data science is about prediction whereas business intelligence is about reporting or drafting a visualised representation of data.
7. Data Science Is Big Data: This is one of the most excited prejudices related to data science. Giving the controversy a more unobstructed view, Big Data is data science, but data science is not big data. You can consider big data as a subset of data science.
8. Data Science Is Magic: Well it might seem so, but technology is never magic. No push buttons or magic wand to provide results. It requires skills and expertise in the domain to do things that appear like magic.
9. Data Scientist Must be Proficient In Coding: Yes they must know how to code, but it does not signify that they must excel. Nonetheless, data scientist need to analyse data and statistics to predict and not sit to code.
10. All Data Owns Respective Value: Partly true but mostly a myth. Just because you have data does not notice that you can solve issues. Not every data aids results. You need to have appropriate data to optimise results.

Smart machines and apps are consistently turning into a day by day marvel, helping us make quicker, more correct choices.
What’s more, with more than 75 per cent of organisations investing in Big Data, the job of AI and machine learning is set to increment significantly throughout the next five years.
Starting at 2017, a fourth of associations are burning through 15 per cent or a more significant amount of their IT budget on machine learning abilities, and we expect the quantity of machine learning models to rise in the near prospects.
Clichés to avoid Machine Learning
Machine learning is a powerful tool for a lot of problems, but it approaches with costs — it can set up some clichés to systems which build up over time and develop into sizeable industrial debt.
We have organised a set of commendations that help us keep away from or reduce clinches in machine learning systems. Machine learning engineers should follow them when generating new Machine learning models and improving new product features utilising an ML solution. So let’s discuss five clichés of Machine Learning that you should avoid!

1. Clean up the scraps

It is one of the best ideas to generate scrappy Machine learning solutions for testing points. But once the framework is demonstrated effective and propelled to 100%, you have to clean it up. Cleaning up scrappy things implies finding a way to make the structure less difficult. Individually, you should set aside the opportunity to unite the framework into existing arrangements if conceivable, and additionally evacuate redundant code and features.

2. Let others examine your plan

When you are planning a machine learning solution, it is imperative to pass on your proposition to other people. You probably won’t understand that there are existing solutions that as of now address the majority of your prerequisites. Regardless of whether that isn’t the situation, incorporating others in the underlying talks can give valuable input to make your framework more straightforward.

3. Report your framework

On the off chance that your frame is hard to document and clarify, it is excessively puzzling, period. Codes are documentation, yet it is documentation at a level of detail that probably won’t be anything but difficult to process. As a dependable guideline, you ought to have the capacity to clarify the critical purposes of the entire framework in 30 minutes or 2 pages.

4. Lack of feature choice

Engineers are inclined to be more keyed up about including new features to the machine learning model but be concerned less about erasing old elements. Previous features may no longer be helpful after a convinced number of iterations, and they create the model harder to recognise and more composite.

5. Try not to utilise a more significant number of features that would typically be appropriate

Engineers should involve in functions choice and model tuning iteratively and model tuning iteratively. They may have the capacity to evacuate many ML includes that include multifaceted nature and calculation overhead while keeping up the quality of the model. It is essential to understand the interrelation of the features and the model. A few features probably won’t have any effect basically because the model is excessively basic, making it impossible to learn them.
Conclusion
There are advantages and flat sides to each troublesome technology, and AI is no particular case to the run the rule. What is vital is that we recognise the difficulties that lay before us and understand our duty to ensure that we can take the full favourable position of the advantages while limiting the tradeoffs.

Data Science is a field that comprises of everything that related to data cleansing preparation and analysis and Big Data is something that can be used to analyse insights which can lead to better decision and strategic business moves, one the other hand Data Analysis involves automating insights into a certain dataset as well as supposes the use of queries and data aggregation procedures.
In the following infographics, we put Data Science, Big Data and Data Analytics on the table to know more about them.

‘Machines can teach themselves.’ This phrase had captured our imagination the day it was coined, and it continues to do so. What kind of algorithms encompasses such a phenomenon? What are the Machine Learning basics? The answers to such questions have been revealed in various capacities over the years, but the curiosity around the subject strives for more.
One of the few misconceptions around Machine learning is that it doesn’t involve human intervention.
Machine learning algorithms are based on python programming and other such languages. These algorithms are not self-sufficient, at least in the initial stages. They are supervised and trained using data sets, to obtain primary outputs in the beginning. Once the algorithms based on python programming mature, they start recognizing complex relationships within data.
To get optimum results, the quality of Data used matters. The training data should be free from misclassifications. Otherwise, it may hamper the learning process of algorithms.  Only a few algorithms can overcome such misclassifications.
The quantity of Data should be calibrated as well. Exposing the algorithms to humongous sets of test-data may make them responsive to a specific information-niches. They may provide inaccurate results when fed with something other than test-data. So, it’s about maintaining a balance between under-training and over-training an algorithm.
Now that we are done with the revelations, let’s understand Machine learning basics. It comprises three essential components:

• Model: This component is responsible for identifying relationships and making predictions.
• Parameters: These are the factors which Model takes into consideration while making decisions.
• Learner: This component is responsible for comparing the predictions made and the actual outcome. Based on the dissimilarities found between the two, it adjusts the parameters.

Let’s understand the Machine Learning basics via a real-world scenario.
Let’s assume that there is a teacher, who wants her students to attain the best grade in a test. She wants to calculate the time her students should devote to their studies, to obtain the desired results. Let’s see how machine learning can help her find the solution.
Firstly, the teacher will set the parameters for the Model. In this case, parameters will be ‘Hours spent on studying’ and ‘Resulting scores’.
Suppose the teacher gives the following relationship between the parameters:

Based on the relationship as mentioned earlier, the machine learning algorithms will form a predictive line of results for different inputs.
Once the machine learning model is established, the actual test results are entered by the teacher. Let’s assume that she enters the scores of four students along with their study-hours.
The above results or scores will act as the training data, through which the learner will refine the Model. It will assess the difference between the predictive results given by the original Model and the actual results. The parameters will be adjusted accordingly by the learner, to improve the accuracy of the Model.
For example, the relationship mentioned above between the parameters may be modified into the following.

As you can see, the predictions have been reworked, to get closer to the actual results. It must be noted that the Learner makes very minute adjustments for refining the Model. The training cycle can be repeated again and again until the perfect Model is created. A Model that can predict the correct scores based on study-hours.
Similar training cycles can be conducted for creating Models that can identify events and objects. There is so much to learn and reveal about Machine Learning that one write-up cannot suffice. Still, we hope that this write-up gave you a good insight into the mysteries of Machine Learning.