AI in IT Operations
With increasingly remote workforces, IT operations groups found themselves scrambling to ensure that their organization had the proper infrastructure and collaboration tools. With an increasing workload, ‘business as usual activities’ may have been moved to the back burner. AI in IT Operations (AIOps) has a key role here in automating day-to-day tasks for operations specialists without sacrificing security, stability, or service. This can be based on solutions developed by various vendors in recent years, which can assist IT operations groups in application performance monitoring, information technology infrastructure management, network performance monitoring and diagnostics, and IT event correlation and analysis.
AI-Backed Automation
Due to economic pressures, companies are increasingly looking for avenues to reduce their operating expenses. AI-backed automation solutions can support these efforts by reducing the need for repetitive, manual and freeing employees to focus on more impactful and rewarding work. Great strides are being made in the use of AI to automate data entry, a costly and burdensome endeavor. A properly trained AI solution can automate and enter data quickly, accurately, and with little human intervention.
AI as a Marketing Tool
AI is also increasingly being used in personalized approaches to marketing, based on patterns of usage and traffic. One example is the appearance of pop-up ads online based on earlier browsing history, which uses machine learning to inform marketing. Similar approaches can be used to develop recommendations for users based on their history of using and recommending products and services.
AI in Financial Trading
In the financial sector, trading is carried out at high speed based on algorithms that have learned from past trades to predict and executive current trades.
Healthcare
Algorithms created based on historical medical data can enable more accurate and rapid disease detection and diagnosis, and can help predict how to potentially avoid hospitalizations based current knowledge of diseases.
Foundations of AI-Backed Analytics: Data Ingestion and Data Inventory
The cornerstone of any Analytics or Data Science department is the data. For an organization to be poised for success, leadership must ensure that proper data ingestion is in place. Data that is brought into an area is generally raw or semi-curated. Machine Learning (ML) algorithms read these data sets to learn from them. Data can be batch loaded, or data streams can be created and ML algorithms applied as the data comes in. The learning algorithm in ML generally runs optimally against raw or semi-raw data; this allows for all of the testing to be done on data that has not been manipulated. However predictive analytics, which is also a subset of AI, works better with curated data, but optimally learns from semi-curated data.
One issue that many organizations face is the lack of a proper data inventory. Neglecting to invest in the construction and maintenance of a data inventory will often lead to unnecessary expenditures, underpowered analytics, and rework for a company’s analysts. Data scientists report that up to 90% of their time is spent collecting, cleaning, and wrangling data. With a properly managed data inventory, organizations can reduce this workload. Managing a data inventory would have to change slightly in order to expose data that has not been fully transformed to fit a data model. Data is taken and exposed from a curated layer (or staging layer) and then moved into an area that allows for ML algorithms to learn by using this data. Lastly, the results are moved into an area for easy consumption.
Effectively Using Data to Emerge Stronger
Key components of effective data use include:
- Segmenting data in a way that allows you to answer the questions you want answered with predictive analytics or ML
- Setting up your system to expose semi-curated data to the correct place for ML to work
- Giving only those individuals who have ML capabilities access to the libraries and systems
- Choosing features, algorithms, training sets (aka data) and evaluation criteria to determine the success of the algorithm.
In conclusion, there are many factors to take into account before initiating an AI project. Liberty Advisor Group has extensive experience with AI applications. Talk to us about your needs.
| Discipline | Description |
| Artificial Intelligence (AI) | The science and engineering of making intelligent machines, especially intelligent computer programs.[i] |
| Machine Learning | A system with the capacity to learn based on training to do a specific task using tracking performance measures. This uses algorithms to learn.[ii] |
| Deep learning | This subset of machine learning applies algorithms using an artificial neural network consisting of layered connections. These connections evaluate and process data to produce the desired output classification. |
| Natural Language Processing | Understands human language and communicates back, an exciting area with applications such as Amazon’s Alexa. |
| Predictive analytics | Uses statistics and modeling to make predictions based on current and historical data. |
| Statistical Modeling | Technically not AI, this involves an array of models that use statistical techniques to formulate analytical predictions or conduct exploratory analyses through data mining |
[i] http://jmc.stanford.edu/articles/whatisai/whatisai.pdf
[ii] https://www.fda.gov/media/122535/download
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