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Data Capture Firm Uses AI to Dissect Georgia Runoff Spending

georgia runoff spending

With majority control of the Senate at stake, it’s no surprise that the recent Georgia runoff elections were the most expensive political race in American history. Wherever campaign dollars flow freely, as they did in Georgia, accountability questions proliferate. Where did that money come from, where did it go, and what influence (if any) did those spending choices have on results?

Election law requires that campaign expenditures be made public. Even so, it often takes weeks or even months for analysts to sift through a dense forest of information and build usable datasets. Important decisions have to wait; only after a dataset has been given the green light can anyone even begin to search for associated outcomes and trends.

Many have wondered whether there is a legitimate place for artificial intelligence in harvesting public data repositories. Can machine-generated data really be trusted? And even if it can be, can it replace human analysts?

When Data is Public but Nearly Useless

Each TV and radio station is required to carbon-copy invoices for political advertisements to the Federal Communications Commission, which makes them public record. This sounds great on paper, but the FCC mandate is to merely publish the invoice documents — and each election comes with tens of thousands of invoices.

The invoices are important because of the dates, callsigns, and amounts listed on them page by page. The data allows analysts to build spending maps and scrutinize campaign behaviors, but the data needs to be aggregated in a spreadsheet first. A spreadsheet analysis would ordinarily mean going through this massive amount of invoices by hand.

A Better Use of Data

Of course, it’s not just the FCC receiving invoices — all businesses receive invoices when they trade with each other. Any company of size faces the same problem as the data analysts who would like to use the FCC data.

One automation software company that solves the problem of translating business documents to data for enterprises decided to unleash its automatic data capture on the FCC invoices for the Georgia runoff elections. The vendor Rossum, in partnership with analysts from the e.ventures fund, just published the resulting data set.

AI-Powered Reporting Follows the Money

Ever since the 1976 film “All the President’s Men,â€� many of us have internalized the admonition to “follow the money.â€� The practice remains a tried-and-true method for discovering unseen relationships and shedding light on patterns of activity and motivations that we might otherwise miss.

Data capture technology cannot read the minds of campaign managers — something for which we can all be thankful. But the new breed of automation based on artificial intelligence might at least enhance our ability to better see the tracks they’ve left.

Accelerating Data Collection

In addition to eliminating the time-consuming tedium of keystrokes and accelerating data collection, policy analyst Jordan Shapiro found that Rossum’s processing of the Georgia spending data produced a greater degree of granularity. This granularity, in turn, enabled her to better grasp the thinking that lay behind decisions made by the various campaigns.

Following the Spend

“As a political analyst, Rossum’s data about 2020 Georgia runoff election spending gave me the opportunity to get inside the heads of the campaigns to see which areas they thought were more or less competitive based on how much each candidate spent in that region,� says Shapiro. “Particularly helpful for my work was the ability to compare county-level spending patterns with a shift in vote share between November and January.�

Data Suggests a Shift from Red to Blue

Southern states have historically been fertile territory for Republicans seeking election, but that appears to be changing.

An NPR report from January 2020 found more Black Americans are moving south, and as they relocate, they are contributing to a shake-up in election results. In 2000, Rockdale County (southeast of Atlanta), for example, was a predominantly white area with a Black population of approximately 18%. Today, that same area has a 55% Black occupancy.

Changing Demographics

Shapiro notes that Georgia’s changing demography, heavy campaign spending by Democrats, increased voter mobilization at the grassroots level, and a tumultuous national political stage all played a hand in what many saw as upset victories. Political turmoil and civic unrest on the national scene rocked Republicans and set the stage for Democratic wins. Both races were reasonably close, with Ossoff winning his race by about 55,000 votes and Warnock winning his with around 93,000 votes.

Regardless, Rossum’s analysis found something even more important than spending-outcome correlations: reasons to default to AI-driven analysis.

Why AI-Enhanced Reporting Will Be Big

The mandatory availability of campaign spending records affords skeptics and naysayers an opportunity to fact-check any reporting that emerges after an election. Reports that have been compiled using AI-powered scanning techniques can be fact-checked just as easily as traditional reports constructed by workers furiously pounding away on keyboards.

As AI-powered processes continue to improve, confidence in this newer methodology is certain to grow as well. As that happens, voters can expect to see more accurate, useful information in the run-up to Election Day.

AI-driven reporting benefits democracy in at least four ways:

1. Enhanced Transparency of Public Data

Much data is the subject of public record, but too often, it is not readily available for analysis and therefore carries only a fraction of its potential. The problem with campaign spending records is that key dates and amounts are scattered through scanned documents instead of being aggregated in a spreadsheet that can be readily analyzed for new insights. This problem is common for many registries, FoIA data releases, and internal government operations.

2. Rapid Data Analysis

The speed advantage of AI-enabled reporting systems over traditional methods of computation will make relevant data available much earlier than in past election cycles. Earlier delivery of results could, in turn, open up new options for campaign managers to consider as constituents respond favorably or negatively to various messages.

3. Added Depth and Surfacing of Less-Obvious Correlations

As noted above, Shapiro was able to take Rossum reporting on election data and cross-reference it with migration data. In doing so, she discovered a trend in specific Georgia counties shifting from presumed Republican strongholds to surprise Democratic wins.

4. More Informed Policymaking and Decisions

Tightening the link between election results and policymaking can serve as a check on any politician’s temptation to drift away from the will of the people they serve. A democratically elected official can only disregard his or her mandate for so long. Faster access to accurate information allows voters more time to assess a politician’s voting record.

What’s Coming in 2022

With another election cycle coming entirely too soon, keep an eye out for new applications of AI. Both parties will use the technology to unearth patterns, analyze results, and suggest new political strategies. Policymakers will check proposals against their constituents’ latest voting trends.

Will 2022’s political environment be any less fraught than the 2020s? Maybe not — but it will be more data-driven.

Image credit: edgar colomba; pexels

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6 Skills You Need to Become a Data Analyst

become data analyst

Data analyst is the process in which the judgment, refining, changing, and modeling data takes place with the aim of making a better conclusion. Data analysis takes part in an important role, it helps in generating scientific conclusions and also increases the regulations in an organization. Here are six skills you need to become a data analyst.

Data-Driven Business Strategies

Today, with the deepening of the concept of data operation, more and more companies are aware of the value of data-driven business strategies and emphasize the participation of all employees in data operations. Developing the ability to analyze data is also a future trend.

A data analyst is someone who accumulates data, processes, and performs analysis of data. He or she can interpret numbers and data into simple English in order to help organizations and companies understand how to make better business decisions.

At its core, data analysis means taking a business question or need and turning it into a data question. Then, you’ll need to transform and analyze data to extract an answer to that question.

Companies of today realize how data-driven strategy is important to them and they are in need of talented individuals who can provide insights into a constant stream of collected information.

Types of Data Analysts

  • Finance
  • Data assurance
  • Business Intelligence
  • Marketing
  • Sales

The types of data analyst listed above are some of the most challenging fields which require a data analyst in their management.

Here are 6 skills you need to become a data analyst.

1. MATHEMATICAL SKILLS

Mathematical skills are important for making a profession in data analysis, as you have to play with numbers and figures all through the day. Necessary mathematics skills include:

*One should be good at calculations

*Measuring and analyzing data

*Having the ability to organize information

*One should know how to schedule and budget

Mathematical skills help in having better problem-solving skills — and also helps in financing our individual businesses.

2. ANALYTICAL SKILLS

As it is called, It is clear that analytical skills are the huge significance of data analysis. These skills include assembling, sorting, and analyzing all kinds of raw data in particular. It also helps you to view provocation or situation from a different angle. Hence, If you want to be a professional data analyst then you need to widen your analytical skills and thinking. Analytical skills help in viewing a challenge or situation from different perspectives. It is one of the most important and efficient parts of the skill you need to become a data analyst.

3. TECHNICAL SKILLS

Good computer and technical skills are very important. The main knowledge of statistics is quite helpful as well as producing languages like python or Mat Lab, and analytical languages like R and SAS are very favorable to know as a data analyst.

The more language you know, the faster and better for you to get your dream job. Examples of technical skills you need to know are:

  • Programming languages.
  • Common operating systems.
  • Software proficiency.
  • Technical writing.

4. DETAILS SKILLS

Data analyst is all about details, It helps a data analyst to be able to find and see any basic unseen errors and links, that is especially important at the point of solving problems and making decisions.

5. BUSINESS SKILLS

Aside from the technical skills, there are some business skills that one should acquire to function as a Data analyst. Some of which are:

Communication Skills:

As a data analyst, you are expected to work with different sets of people in your team which makes your communication a really important part of your job. Working well together as a team for the benefit of your organization is also one of your main responsibilities and skill.

You should be able to communicate effectively with the teammates to prepare, present, and explain data. Communication is key in collaborating with your colleagues. For example, in a kickoff meeting with business stakeholders, careful listening skills are needed to understand the analyses they require.

Similarly, during your project, you may need to be able to explain a complex topic to non-technical teammates.

Time Management and Organizational Skills:

As it is expected of you to work with different people in your team. You should be able to manage your time including your responsibilities, as well as meet your deadlines.

Decision-making and Problem-solving:

These skills are the bottom line of data analysis. The main job of a data analyst is to give the right information for decision-making and problem-solving process. Which is why it occurs to be a perfect skill required to be a data analyst.

You might need to research a quirk of some software or coding language that you’re using. Your company might have resource constraints that force you to be innovative in how you approach a problem.

The data you’re using might be incomplete. Or you might need to perform some “good enoughâ€� analysis to meet a looming deadline.

6. DOMAIN KNOWLEDGE

Domain knowledge is understanding things that are specific to the particular industry and company that you work for. For example, if you’re working for a company with an online store, you might need to understand the nuances of e-commerce. In contrast, if you’re analyzing data about mechanical systems, you might need to understand those systems and how they work.

Domain knowledge changes from industry to industry, so you may find yourself needing to research and learn quickly. No matter where you work if you don’t understand what you’re analyzing it’s going to be difficult to do it effectively, making domain knowledge a key data analyst skill.

Remember that Domain knowledge is certainly something that you can learn on the job, but if you know a specific industry or area you’d like to work in, building as much understanding as you can upfront will make you a more attractive job applicant and a more effective employee once you do get the job.

Once you have the skills mentioned above — you can use them to attract attention to yourself so as to get your dream job. These are the criteria you need for the data analyst skills checklist.

Academic Background

A bachelor’s degree is often necessary, not all the time though. To work as a data analyst you need to acquire a degree in any of these Science courses.

  • Economics
  • Business information systems
  • Mathematics
  • Statistics
  • Computer science

As much as the above degrees are important for your success — what matters the most are the skills that you possess. Right now during the COVID-19 problems may be the time for you to upgrade yourself and your skills.

Conclusion

Data analysis is speedily growing field and skilled data analysts are huge in demand throughout the country, in every industry. This means that you would find many opportunities only if you are exceptional and show your excellent data analytic skills.

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