Diving deeper into the drivers of AI & Data Science solutions implementation two patterns emerge: internally focused drivers versus externally focused drivers. Internally focused drivers are drivers that aim to boost organization’s operations by enhancing productivity, decreasing manual workforce labor, and increasing efficiencies. On the other hand, externally focused drivers fall into two main categories: first, augmenting customer retention and acquisition, and second, augmenting organization’s growth strategies. Thus, a comprehensive list of AI & Data Science solutions falls under the three aforementioned drivers:
- AI & Data Science solutions to boost operations;
- AI & Data Science solutions to augment customer solutions; and
- AI & Data Science solutions to strengthen growth strategies.
The most important discourse when it comes to understanding AI & Data Science solutions adoption within a corporate framework is the underlying objectives. Our services capabilities remain specific and focused on the industries where our impact can be delivered, and objectively expect positive results.
- Sector-Specific AI & Data Science solutions Understanding: Surprisingly few companies know where and how AI & Data Science can create value. We provide our clients with conceptual understanding of what kinds of problems AI & Data Science solutions can solve. This is an important step in choosing the appropriate applications that attempt to solve the client’s problems with sufficient technology while providing value in measurable ways. We also take into consideration the overall organizational readiness for embedding new technologies in existing workflows;
- Clarity on Goals: We respond to the need to understand that any “reimagining” of our client’s workflows through AI & Data Science solutions is an enormous project with varying rates of success with the cost factor. We therefore help organizations in their need to focus on gaining AI & Data Science solutions capabilities first and then use them as a measure for defining mid-tem and long-term goals;
- Organizing Data-Compatible Teams: Once the capabilities have been properly explored and understood, and those capabilities aligned with the client’s goals, the next step we take with our client(s) is to assemble data scientists and subject-matter experts to create multidisciplinary teams. Subject-matter experts in this case are employees with a deep understanding of business processes in a particular function or division. In assisting our client organize data-compatible teams, the following considerations are made:
- Ensuring that the data scientists working on the solution are clearly aware of the organizational problem AI & Data Science solutions is being applied to (for-profits or non-profits respective use cases);
- Subject-matter experts ought to be able to translate organizational problems into data questions. Then, data scientists might be better suited to identifying if AI & Data Science solutions can solve those respective data questions raised;
- Since data projects (solved by AI & Data Science solutions) are not a one-time investment, when organizations generate new data, the algorithms need to be fine-tuned in order to incorporate the additional data and still maintain accurate results. Maintaining and updating the AI & Data Science solutions systems is a necessity, and organizations need to assemble and maintain teams that can accomplish this task even when the project is largely developed and deployed.
- Data Infrastructure Considerations (Data-centered Strategy): Data science is the easy part; getting the right data, and getting the data ready for analyses, is much more difficult. We therefore help organizations in reimagining how they are collecting, storing, and managing data and this remains a decision made after gaining a certain level of data competency. Once the data being tested in a pilot is measurably valuable as a proof of concept, we assist organizations in thinking through the next phase in which the entire data infrastructure is overhauled. The three(3) main challenges we help organizations solve at this point are:
- The difficulty in accessing data, which is usually harder than anticipated;
- Data labeling and classification as even the data that is accessible immediately is not usually stored in a format that makes it easy to use;
- Updating and/or upgrading the storage hardware.
- Cost implications assessment: We essentially advice and take organizations through the several real-time and periodical cost implications of adopting and implementation of AI & Data Science solutions. The three key advantages we give our clients through this assessment include:
- Giving organizational focus on building skills rather than looking for outright returns immediately;
- An objective understanding that certain AI & Data Science solutions might not require total data infrastructure overhauls in order to successfully test and deploy;
- Build internal confidence in data management capabilities before heavily investing in a AI & Data Science solutions solution.
- Predictive Analytics Maturity Assessment Framework: Lastly, we provide our clients with an internal assessment framework for post-service internal examination of the maturity of the client’s current Predictive Analytics (PA) environment which looks into issues such as:
- Its data and technology readiness;
- Its tool selection;
- Its adoption of modeling techniques; and
- Its deployment and integration to decision systems.
With the information provided by our assessment framework we provide, the client can continuously track and benchmark its current and progressive analytics initiatives and infrastructure at both departmental and enterprise levels, assess its decision-making effectiveness and the alignment of its Predictive Analytics activity to overall organizational objectives, and identify opportunities to share local pockets of analytic DNA across the organization as a whole.
- AI & Data Science risks management: While AI & Data Science solutions generate consumer benefits and business value, they are also giving rise to a host of unwanted, and sometimes serious, consequences, which could range from the data fed into AI systems to the operation of algorithmic models and the interactions between humans and machines. We hence help organizations in confronting such potential risks by first illustrating a range of easy-to-overlook pitfalls, then presenting frameworks that will assist them in identifying their greatest risks and implementing the breadth and depth of nuanced controls required to sidestep them. Finally, we provide an early glimpse of some real-world efforts that are currently under way to tackle AI & Data Science risks through the application of these approaches.
- Build predictive analysis models & simulate market events;
- Store different data for future analysis;
- Track trends, respond to issues, monitor product launches & enhance brand perception through sentimental analysis;
- Automate risk-credit management;
- Fighting fraud through such parameters as account balances, spending patterns, credit and employment details;
- Get a clear understanding of customers’ business operations, transaction histories and assets;
- Get to know who the really customers’ are hence building of customized products & run relevant campaigns.
- Personalized marketing and website optimization;
- Disciplined analytics to predict customer behavior at the micro-market level, optimize product availability & price to maximize revenue growth;
- Understanding customers’ perceptions of product value & accurately aligning product prices, placement and availability with each customer segment;
- Build energy profiles for hotels without sacrificing guest comfort;
- Brand monitoring through ML and NLP (Natural Language Processing) solutions to keep up with the speed at which people exchange info on the hotel services;
- Predictive (condition-based) hotel maintenance using sensor data.
- Reduce healthcare costs by drilling down to the trends in room usage & required resources available to cater to patient needs – identify potential areas of operational gaps and revenue losses;
- Drug discovery through various sets of structured and unstructured biomedical data;
- Simulation of how new drug(s) would interact with body proteins and predicts the rate of success;
- Provide accurate insights into genetic issues arising out of specific drugs & diseases;
- Encourage self-health management through wearables that record important health readings like blood pressure, heart rate, sleep pattern etc.;
- Process extensive clinical & laboratory reports to conduct quicker & more precise diagnosis through deep learning technique;
- Diagnose chronic diseases at early stages & identify treatment options that have proven success records;
- Optimize supply chain & review equipment maintenance schedules to prevent unexpected breakdowns.
- Use purchase data (real time & historical; online and offline) to predict inventory needs based on the season, day of the week, activity at store area etc.;
- Rely on predictive analytics to improve end-to-end customer experience;
- Gain customer feedback & market insights through sentimental analysis of product reviews, call center records & social media streams;
- Identify cross-selling opportunities & shopping trends;
- Gain insights on post-purchase use;
- Personalized offers to respond to specific customer expectations;
- Real-time inventory management and tracking;
- Support demand-driven forecasting.
Our solutions assist Non-Governmental Organizations and Non-profits in measuring project-level or organization-wide performance through the creation of results frameworks, collection of quantitative and qualitative data across projects, and evaluating performance through analytics and dashboards. A customized combination of NBI (Non-Profit Business Intelligence) and Data Science tools are leveraged upon to measure performance using organization’s performance management plan (PMP), M&E frameworks and key performance indicators (KPIs).
Typical solutions in amplifying Research, Monitoring & Evaluation:
- Sentiment analysis to categorize mood of online conversations;
- NGO Expenditure and accountability tracking;
- Disparate datasets integration to better understand unit performance;
- Strategy development on collecting and analyzing “little data” and “big data”;
- Predictive analytics for better understanding of sectoral challenges;
- Indicator Development and tracking (Output Indicators & Outcome Indicators);
- Results-based monitoring and evaluation (M&E) dashboards.
- A complete view of agency performance that combines personnel budgeting, operation budget and capital budget creation, cost allocations, forecasting and planning, reporting, and performance-based budgeting into a unified framework;
- Combined formal cost-based budgeting with performance-based goals for real-time evaluation of cost structures on the basis of outputs they generate hence ease in establishing a system-wide conformity that flags performance variances;
- Secure, collaborative, enterprise-scalable reporting and process automation solution that enables users to merge enterprise data with focused narrative analysis in a controlled, auditable environment. Multiple participants in different departments can collaborate in the assembly of complex reports, working independently while ensuring that proper controls and approvals are in place
- A Non-profit Business Intelligence Toolkit that optimizes manual and Excel-supported processes, and modernizes existing custom or rigid tools like Excel, VBA, SQL, Access, and others that are used in budget execution, formulation, and reporting tasks. Such Toolkit shall integrate data outputs from core financial, compensation, and other resource management systems, eliminating the need to cleanse and re-enter data, improves accuracy, quality, and auditability of analysis, budgets, and forecasts by providing single version of truth for your financial, project, and resource data;
- Pervasive Data Analytics applications to help the client government agency reduce fraud, waste, and abuse.