OpenText’s Senior VP of Professional Services Prentiss Donohue outlined the seven main steps of helping artificial intelligence (AI) and machine learning reach its full potential in the information age.
Machine learning (ML) and artificial intelligence (AI) are moving from being enterprise catchphrases to broader enterprise use.
In fact, the interest surrounding the adoption and strategies similar to the tipping point and cycle for enterprise cloud strategies four years back when companies did not have the option of moving to the cloud and the only question remaining was how?
And when? Ml and AI strategies appear to be following the same evolution mode as more companies establish their approaches. Here are some of the thoughts revolving around how.
Recently, Forrester revealed that nearly two-thirds of decision-makers in enterprise technology are either currently implementing, have implemented or are growing their use of AI.
This effort and exercise are propelled by the data lakes that are held in companies which thanks to low-cost storage and compliance are usually sitting idle.
For this reason, taking advantage of such rich repositories in a bid to get AI to respond to the questions that we may not know how to ask or we are not asking is the reward that businesses must understand.
According to details from International Data Corp, enterprise expenditure on AI technologies is anticipated to reach more than $47 billion in 2020, which will mark a considerable increment from $8 billion back in 2016.
Furthermore, organizations across different sectors are expected to continue adopting artificial intelligence and machine learning technology in the coming years while transforming not only their main processes but also business models in a bid to leverage the potential of ML systems for both greater cost efficiencies and improved operations.
As more business leaders embark on establishing strategies for how to best take advantage of this groundbreaking technology, it is imperative for them to bear in mind that AI and ML adoption is a journey as opposed to a race.
For this reason, you need to consider these following steps for your data science projects:
Table of Contents
Define a Use Case
Business leaders and their project managers have to start by taking time to define clearly and articulate the specific challenges or problems that they would like artificial intelligence (AI) to solve.
In fact, the more straight to the point their goal is, the higher their chances of implementing AI successfully.
For instance, stating that the company would like to boost its online sales by 10% is not adequately specific.
A more defined or rather specific statement such as “aiming to increase online sales by 10% through monitoring the demographics of site visitors “should get the job done as far as making sure stakeholders understand it and articulating the goal are concerned.
Prove Data Availability
After clearly defining the use case, make sure that systems and processes already implemented can track and capture the data required for performing the necessary analysis.
A huge amount of effort and time is spent on data wrangling and investigation. Hence, companies or organizations ought to ensure that the appropriate data is being gathered not only is adequate volumes but also with the ideal features or variables like ethnicity, gender, and age.
In this case, bear in mind that the data quality is as important to a positive outcome as its quantity or volume.
Conduct Basic Data Exploration
Although it may be tempting for an enterprise to engage headfirst into a model creating initiatives, it is vital that it first conducts a rapid data exploration exercise in which it can authenticate both its data understanding and assumptions.
By accomplishing this, it will be in a better position to determine whether the data is giving the right story on the basis of the company’s business acumen and subject matter expertise.
This exercise will aid the organization in understanding what the important features or variables ought to be as well as the kind of data classifications that have to be established for any potential models.
Define a Model-Creating Methodology
Instead of focusing on the final outcome to be achieved by the hypothesis, it is vital to concentrate on the particular hypothesis itself. Conducting tests to check if the features or variables are most important will assist in validating the hypothesis and bolstering its execution.
Domain and business experts have to be involved, as their continuous response or feedback is fundamental when it comes to ensuring and validating that all stakeholders understand.
Also, since the success of any ML model relies on the successful feature engineering, a subject matter professional is more important than an algorithm as far as creating improved features is concerned.
Define a Model-Validation Methodology
The definition behind performance measures will aid in the comparison, evaluation and analysis of outcomes from numerous algorithms, which, in turn, assist in refining specific models further.
For instance, classification accuracy such as the number of the right predictions made divided by all the total predictions made, then multiplied by 100, can be a good performance measure when dealing with a classification use case.
In this case, you will need to divide data into two separate data sets, i.e. a training set, which is the set on which the algorithm will be trained whereas a test set is the data set on which the algorithm will be assessed.
This task is as easy as picking a random split of data even though it depends on the algorithm’s complexity. Furthermore, domain and business professionals ought to be involved in validating the findings and making sure that all is moving in the right direction.
Production and Automation Rollout
Upon building and validating the model, it then has to be moved to production.
Start with a limited rollout of several months or weeks, upon which enterprise users can deliver continuous feedback regarding the model’s outcome and behavior.
Once that is done the model can then be launched to a broader audience.
The ideal platforms and tools ought to be picked for automating the data ingestion. Also, systems should be implemented to ensure that the results are distributed to the right audiences.
What’s, more, the platform has to offer multiple interfaces to account for various degrees of knowledge among the end-users of the organization.
Continue Updating the Model
After publishing and deploying a model for use, it must be monitored regularly since by comprehending its validity, a business will be in a position to upgrade the model as needed.
Models can become obsolete due to numerous reasons, for example, a shift in market dynamics, the enterprise itself or even its business model.
Models are established on historical data to allow the prediction of future outcomes. The shifting away of market dynamics from the way a company has always conducted its business can cause a model’s performance to decline.
For this reason, it is necessary to stay mindful of the particular process that has to be followed in a bid to make sure that the model remains updated.
Enterprise artificial intelligence (AI) is increasingly moving past the hype and into reality. Furthermore; it is expected to have a considerable impact on business efficiencies and operations.
Currently, spending more time in planning AI implementation will help businesses enjoy the benefits of technology in the long run.
