Over the past few years, many companies have leveraged technology to pivot to mostly remote workforces. This has made organizations big and small more comfortable with adopting emerging tech and integrating it into their daily operations. By laying the groundwork for effective data utilization, artificial intelligence (AI) has emerged stronger and addresses an uncertain future. Whether for research purposes, parsing through large datasets, automating routine or tedious processes, or improving cybersecurity measures, AI has quickly proven to be a critical tool for organizations of all types and sizes.
In this comprehensive guide, Liberty Advisor Group’s technology experts give high-level analysis of how various industries can leverage AI to fuel growth and cut costs all while retaining business integrity.
- AI in IT Operations
- Leverage AI-backed Automations
- AI as a Marketing Tool
- Foundations of AI-Backed Analytics: Data Ingestion and Data Inventory
- How to Leverage AI Types and Use Data Effectively
How to Leverage AI in IT Operations
With increasingly remote workforces, IT operations groups once 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 emergent vendors, which can assist IT operations groups in:
- Application performance monitoring: Gather vast amounts of data in various forms and sift through it in an instant. With so many types of information these days, manual data and application performance analysis almost requires AI assistance to help predict issues before they happen.
- Information technology infrastructure management: This refers to everything that helps build, maintain, or improve IT services within an organization. AI tools have proven adept at identifying and preventing IT issues, managing devices across networks, and more.
- Network performance monitoring and diagnostics: Ensuring your network is safe and accessible for those that are approved is critical in maintaining operational efficiency.
- IT event correlation and analysis: AI helps with problem-solving analysis to get to the root causes of operational IT issues.
Leveraging AI-Backed Automation
Due to economic pressures, companies are looking for avenues to reduce their operating expenses wherever possible. AI-backed automation solutions can support these efforts by reducing the need for repetitive, manual work 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 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.
In addition to providing deeper insights into consumer behavior, AI marketing tools can:
- Monitor overall brand sentiment
- Help identify issues in reputation management
- Schedule social media posts
- Assist in content creation research phases
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 on 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.
How to Leverage AI Types and Use Data Effectively
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.
|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|
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