How Companies are Using Data to Provide Service During COVID-19

What is customer data and how can businesses use this to provide better services during the COVID-19 pandemic?

By now we hope you’ve heard of COVID-19, a strand of the coronavirus that has swept the world, resulting in many left indoors, self-isolating, and in many countries now under quarantine. 

Small and large businesses all over the world have been forced to shut, with only essential stores such as supermarkets left open to reduce the spread of the virus. 

However, some businesses such as restaurants are still allowed to provide takeaway services, some online stores are still delivering, and in some cases, other businesses are still operating but under strict social distancing measures.

This article will outline customer data and how you can use this to provide service during the COVID-19 pandemic. 

Let’s begin by defining customer data. 

What is customer data? 

As a business, you have access to customer data, especially if your business operates online or has a business smartphone app.

For example, customer data may be generated when a customer signs up to your mailing list, this includes their e-mail address, full name, physical address, and phone number.

This customer data can then be used to provide future updates. This may involve exclusive deals, leaflets delivered to their door, or text messages informing them of your latest product.

Other examples of customer data include:

• Previous purchase history

• Demographic

• Social graphic

• Geographic

Let us explain these in more detail.

Previous purchase history

When a customer places an order, either online or in-store a receipt is often sent to their email address. The business then has access to the buyer’s previous purchase history.

This allows you to send the most relevant/similar products as a follow-up, encouraging them to make another purchase.

Demographic

Examples of demographics include age, marital status, race, income, and education. Occasionally, businesses gather this data when selling their product/service.

This too can be used to generate further sales with a repeat buyer. Likewise, knowing a customer's age allows you to send the most specific products – perhaps generating a list of ‘young trendy products’ to the younger generation and ‘more traditional products’ to the older generation.

Social graphic

Social graphics targeting others who also purchase from the same place, or would be interested in making a purchase.

For example, gaining the attention of your friends or family members who too may be interested in making a purchase.

Geographic

Geographic customer data works based on your location. For example, let’s say your registered with Starbucks, if set up you will receive a notification whenever near a store.

This is done to increase sales for the business, yet at the same time you just can’t resist the temptation to get a Starbucks – it’s now in your mind.

So, how can we use this customer data to provide service during COVID-19?

As you can see, there is a wide variety of customer data you most likely have already gathered and can use.

Knowing individuals geographic location can be especially powerful. Let us provide two examples.

Let’s say Courtney lives in London, a massively impacted area of the coronavirus. Chances are Courtney can’t leave her house to get groceries, especially if she is self-isolating with symptoms.

Already having Courtney’s customer data you can send an email or text to check in with Courtney – make sure she’s got everything she needs.

If you sell essentials then you can offer these to her, increasing your sales and helping Courtney during these tough times.

It’s a win, win for everyone. 

Our second scenario is Pam, Pam lives in the countryside. Although Pam is not socially isolating with symptoms, the nearest supermarket is over twenty miles away.

You can use previous order history to check in with Pam – see if she needs these delivered again.

Likewise, if your business is an essential service, perhaps medical then those in the local area may receive a notification informing them of the services you are offering – providing help to those in need during the current crisis.

We can use this data science to support those during the coronavirus, using past analytics to determine those most in need or those most likely to purchase your products.

How are other companies using customer data to continue delivering their services? 

Other companies are able to follow-up with clients using applications such as Zoom, FaceTime, and email to check in with clients.

Making sure they and their families are okay and following through with business.

Although there is a global pandemic many businesses still need to operate, especially smaller ones to prevent them from going bust.

Remote work including meeting through applications such as Zoom reduce costs whilst allowing business to still take place – perhaps something we’ll see more of even after COVID-19 is long gone.

Likewise, other businesses are putting together small care packages that can be ordered, containing both essentials and luxury’s for their loved ones – letting them know that although they may not be physically together someone out there cares.

Would you like to find out more about how your business can use customer data? 

If you’re struggling during these unprecedented times and would like to find out more about how you can use customer data science and analytics to provide service during COVID-19 then we’d love to hear from you.

You can book a free 30-minute consultation by clicking here.

The bottom line 

Customer data is one of the most powerful yet overlooked tools available to the majority of businesses.

Not only are we able to promote niche products directly to a person’s email inbox but we can also tailor these, increasing the likelihood of them making a purchase.

However, this can also be used to benefit both your business and the customer during the current coronavirus pandemic.

Customer data can be used to follow up with clients, to ensure they’ve got everything they need and if you can provide anything for them, and in some cases provide essential services (granted this is something your business does).

ABOUT THE AUTHOR

Germar Reed, Senior Advisor, Cheif Data and Analytics Office – at GM and Principle at District Analytics, bringing more than 15 years of data-driven marketing and advanced analytics experience to the team. He has extensive experience in developing and applying database marketing strategies for many Fortune 500 companies across a variety of industries, including financial services, technology, retail, automotive and healthcare. Throughout his career, he has been associated with the development of many well-known relationship marketing brands and customer loyalty strategies.

5 Steps to Getting Started With Big Data for Small Businesses

So, you must have heard something about big data. The information you received may have made it seem as though big data is only for large corporations.

Big data is large amounts of data sourced from a business’ activities. It is not normal data hence; it cannot be analyzed so directly. It is true that big data is more suited to large companies. However, this is because big data analytics are known to be costly and time-consuming.

Thankfully, times have changed and developments in business now make it possible for everyone to benefit from big data. As a small business owner, it is essential to take part in its great benefits. This is made possible by the application of suitable tools and techniques.

Read on to learn 5 effective steps to getting started with big data for your small business.

1. Know your customers’ preferences

Data analytics starts by gathering the data acquired from customer activities also known as transactional data. With big data, you can develop highly customer oriented services because you know what they want. Start by gathering data on their experiences and behavior from any device such as laptops or phones.

2. Create a system that can identify trends

Trends in business tell you what is going on with sales, satisfaction, and so on. To benefit from big data, you must create a system that displays important information also known Key Performance Indicators (KPI’s). The system should be efficient enough to identify trends which occur in the market. A business analyst can be of great help in this area.

3. Invest in data solutions

Yes, the cost is the primary reason business owners avoid using big data, but that is hardly necessary seeing as every business requires a good investment to be successful. Invest in some data solutions to enhance your methods of acquiring, analyzing, and interpreting your data. Suitable examples for small businesses are SAS, Google Analytics, IBM Watson Analytics, and much more.

4. Know what your needs are

The tricky thing about big data is that it can provide accurate information but can also be misinterpreted. If your needs or questions for the data are not defined, the information may become useless or part of bad decisions. Take the time to review every department in your business and determine their needs. This will help you with proper analysis and interpretation. Some questions you may develop include:

• Who are our best customers?

• What do customers want?

• What brands get the most attention and why?

5. Take action

The reason why you should use big data is to get results. Results do not come from inaction. After acquiring, analyzing, and interpreting your data, the next steps should be geared toward achievement. What do you do with the information provided by the data?

Big corporations, who use big data, take action to enjoy the following benefits:

• To gain a competitive advantage by tailoring services to customer’s needs.

• To make effective business decisions

• To mitigate risks.

• To monitor business performance

What is Big Data?

Big data is a term common to both large and small businesses. Almost every business owner has heard something about big data, but not all know what it really is.

Big data can be understood just as it sounds; it is data in large sizes. It is aptly defined as a large amount of data in both structured and unstructured forms. Big data is so named because it cannot be analyzed with traditional data processing methods or applications. It requires a different approach and software.

Companies value big data because it provides answers that increase the efficiency of a business.

The concept of Big Data

Big data is characterized by three 3Vs. Each part explains the concept of big data and how it is relevant to your business.

• Volume- Volume is the most important aspect of big data definition. It is sourced from various aspects of a company. The volume of big data can be hundreds of petabytes about business transactions.

• Variety- Variety describes the unlimited forms of data. Data can be in structured or unstructured forms such as numbers, text, and so on.

• Velocity- Data can be received in split seconds. It can also be received in long hours. The velocity of data simply refers to the speed with which it is received and analyzed.

The use of Big Data

Not every business or company can use big data. Smaller businesses can easily perform data analytics with traditional software applications. Big data stands out because its uses are also highly significant. Here are some important uses of big data.

Customer Satisfaction

In any organization, customer satisfaction is vital. Big data is used to gather accurate information on customer needs and preferences from surveys, social media, calls, and so on.

Product Development

Product development is not to be taken lightly in any business. It is important to anticipate consumers’ demands to avoid loss and rejection. Big data analytics helps companies to predict the response of the consumers to a new or modified product.

Efficiency in Operations

The availability and proper analysis of data in an organization helpto improve efficiency in operations. This is the most significant influence of big data on companies. Efficient mode of operations guarantees customer satisfaction and sustainability.

Challenges of Big Data

Getting to know about big data goes beyond its definition; it also involves knowing what challenges lie in using or receiving big data. Despite its invaluable uses,the issues faced with big data have been existent for a long time. Companies still struggle to keep the paceand find effective solutions to them.

Storage

Big data increases steadily with almost no way to control it. Organizations have always had to search for valid ways to store big data because it cannot be discarded.

Time and analysis

Big data is BIG. This means more time, effort, and different methods will be applied in analyzing the data. While this cannot be helped, it is a standard issue because time is important to an efficient organization.

How to Analyze Data

After your team and data analyst have finished setting your objectives and gathering data you need to analyze your data to meet your objectives. When analyzing data you can use descriptive, visual, inferential, or modeling techniques. In this article we discuss various data analysis techniques and tools to use in analyzing your data.

Summarizing Data Using Descriptive Statistics

Descriptive statistics help you summarize and understand your data. There are different techniques for summarizing your data depending on if your data is categorical or continuous. Categorical data refers to observations that fall into distinct categories for example male or female. Continuous data refers to observations that do not have any distinct categories such as weight. 

When your data is categorical the most useful descriptive technique to use is count. You count the number of observations that occur in each category. For example, when you have one variable such as gender you count the number of people who are male and those who are female. When you would like to know the number of people in each category as a proportion of the total you use a percentage. In the gender example we can calculate the percentage of those who are male and the percentage of those who are female.  

As you summarize categorical data you are not limited to one variable. To summarize into categorical variables we use a cross tabulation. In a cross tabulation one variable forms the rows and the other variable forms categories. We then count the number of observations that fall in each category. If in our example we also have an education variable we would be interested in knowing the education levels of males and females. These education variables could be defined categories: no education, primary, secondary, college and university.  

For continuous variables there are descriptive measures that tell us how our observations cluster around a single value and those that tell us how our observations are spread. The mean and the median are two common measures that are used to summarize data. The mean is an appropriate measure when we have observations almost falling on either side. The median is an appropriate summary when we have most observations falling on one side such as our observations are skewed. 

If we collect observations on weight of adult patients we can use the mean to get the typical weight of a patient. If we collect observations on salaries we will have a few people earning much more than others, in that case the median would be a better summary. 

The minimum, the maximum, the range, and the standard deviation tell us how observations are spread. The minimum tells us the lowest observation, the maximum tells us the highest observation, and the range gives us the difference between the lowest and the highest observation in our data. The variance and the standard deviation tell us how a mean value varies. 

The confidence interval is calculated from the standard deviation and it gives us the upper and lower bounds of a mean value. When you have two continuous variables a correlation coefficient helps you understand the strength and direction of relationship. 

A negative coefficient shows you when one variable increases the other variable decreases. A positive coefficient shows you when one variable increases the other variable decreases. A correlation value close to zero shows you there is weak or no relationship. A value of 0.5 shows moderate strength while a value close to 1 shows you there is a strong relationship.

Visualizing Data With Graphs

There are different tools for visualizing categorical and continuous data. To visualize categorical data you use a pie chart or a bar chart. A pie chart divides a circular shape into angular portions that enable you to see the count or percentage of observations that are in each category. A pie chart can only be used to visualize one categorical variable. A bar chart helps you visualize categorical data using vertical or horizontal bars that show you the count or percentage of observations in each category. 

You can add the count or percentage of each category on the bars for easy comparison. Bars that are taller than the others show more observations in those categories. A bar chart can be used to summarize one or two categorical variables.

To visualize continuous observations you can use a histogram, a box plot, a scatter plot or a line plot. A histogram uses bars similar to a bar chart to visualize continuous observations. The key difference is that bars in a bar plot are for a single category while bars in a histogram show a range of values. A box plot summarizes data using a box and whiskers. The whiskers on both ends of the box plot show you the minimum and maximum observations in your data. Observations that lie beyond the whiskers are outliers.

The box shows you where half of your observations lie and within the box there is a line that shows you where the median lies. The histogram and box plot are useful for visualizing the distribution of your observations. The scatterplot helps you visualize the relationship between two continuous variables. It helps you visualize the direction and strength numerically shown by a correlation coefficient.

Making Inferences From Data

The techniques we have discussed so far help you summarize your data. To test hypotheses about your data you use inferential techniques. There are different techniques for continuous and categorical variables. 

A Chi-square test helps you test if there is any relationship between categorical variables. For example, in summarizing categorical data example we can use a Chi-square to test if education levels of men and women differ. For continuous variables we are mostly interested in the mean, where we can use T tests or analysis of variance (ANOVA). 

There are three variants of the T test that help us test if the mean of one variable differs from a target mean, if the means of two variables differ and if the mean of one variable differs at two different time points. ANOVA extends T tests by helping us test if more than two means are different. 

To help support the process of data analysis your data analysts will use both commercial and open source tools have been developed. Popular commercial data analysis tools include IBM SPSS, SAS, Stata, Excel, and Minitab. These tools provide a graphical user interface and a programming language for data analysis. R is a popular open source tool that is used to analyze data by writing programs. All of the tools and techniques we have mentioned support all the data analysis techniques we have discussed.

How Big Data Can Be Used for Your Business

The expression "data analyst" summons pictures of a solitary expert working alone, applying obscure recipes to boundless measures of information looking for valuable insights. Information investigation is not an objective in itself. The objective is to use the information to empower your business and develop and craft strategies that improve operational efficiency and profit margins.

In this article we take a look at how an experienced data analyst in Washington, D.C. is qualified to give your business a competitive advantage against your competitors through the analysis of big data. 

Embracing Big Data for Your Business

Big data is an all-inclusive term that often refers to large amounts of information. Traditional data is typically relational data found on internal databases. Conversely, big data is collected and generated in large volumes and varies in frequency, variety, volume, and value.  

In most small to medium sized businesses big data is a moderately undiscovered resource that organizations never exploit yet should. Big data does not simply provide trendy information and statistics, but rather genuine case studies that uncover what's going on now, and what is prone to happen in the future in each niche group. These glimpses into the future provides your business with the opportunity to gain insightful projections that prove valuable during quarterly planning events and development. 

Big data use and implementation is best observed in the accomplishment of online powerhouses, such as, Amazon, Google, Facebook, and eBay. Through proper big data sorting, development, and monitoring each online influencer provides a detailed account of just how effective and useful big data is. For example, through the analysis of big data Facebook is able to position local businesses in front of consumers most likely to buy based on online behaviors, locations, and interests. Likewise, Amazon provides detailed shopping recommendations based on transactional coincidences, search, and behaviors within the website. 

Understanding Big Data

Without the proper application of big data many companies fail to harness the power of innovative databases that are readily available to them. To understand how big data is utilized in general you can equate big data analysis as the same process employed for drilling oil. When an oil rig works to drill for oil it begins by slowly chipping away at the surface. Once the surface is successfully penetrated with the least amount of surface or environmental disruption the rig begins to drill deeper and harder. Often the deeper the drill digs the more accelerated the pace. 

As so it is with big data. The more information there is to sort and analyze the slower the process is likely to be. Initially, identifying the type, variety, and volume of the data sets the tone on the types of outcomes to expect from the data and how it is most likely to benefit your business.

As you undertake the task of gathering, sorting, and analyzing big data remember to allow your data analyst in Washington D.C. to work at a pace that makes sense for your business, objectives, and information. Consider that our digital era produces an alarming amount of data and information every second. The rate at which this information is gathered and sorted duplicates at regular intervals, consistently daily. 

Types of Use for Big Data Findings

Once your business has embraced the value of big data you must determine what to expect and identify tangible outcomes that can be derived from this data. Begin by asking general questions such as what sort of significant worth big data in transactions and shipping are likely to provide. Or asking particular questions to better identify your target market.

From a quality perspective, the utilization of big data is often categorized as one of three measurements: 

Effectiveness

Operational effectiveness directly affects the way your business runs, interacts with customers, and supplies your products or services. For this situation, information is used to settle on better choices, to upgrade asset utilization, and to enhance process quality and execution. Ensuring that your business is effectively operating and serving customers efficiently is the first step in keeping your company competitive and savvy within your industry. 

Often discovering how your business is operating and identifying various areas of improvement saves time, reduces downtime, and conserves your resources. As your data analyst works through big data trends and outcomes, various patterns emerge that offer key points on how to simplify your operations for optimal interactions with each transaction or project.

Experience

The second measurement is client or customer experience. The largest point and purpose is to increase or improve customer loyalty, perform exact client segmentation, and improve client or customer handling. Big data pushes CRM strategies and helps all customer oriented practices to be relevant, controlled, and measured.

Likewise it also empowers you to discover new plans of action to supplement income streams from existing items, (also known as upselling), and often propels thoughts of innovation to improve customer experiences and sales funnels. When using big data to improve the way your customers or clients interact with your business you gain valuable insights that help you to strengthen your brand and authority within your industry organically as well. 

Marketing

Data driven advertising continues to grow in popularity as more and more business turn to digital marketing. When big data is used to identify target audiences the return on investment is often positive. Marketing data sheds light on how your business can create relevant messaging, identifies proven pain points that your company solves, and gives you a competitive edge against your competitors. Big data is compiled in many ways such as via mobile behaviors or website traffic patterns.

Utilizing data gathered based on how visitors interact with your website, how many conversions your sales page secures, or how long a user spends on a webpage on your website provide valuable tips on how to build your next campaign.

Big data is one of the most essential parts of any business. As your business begins to embrace the use of big data, you are sure to learn more about your business than ever before. Some of the most simple and reliable ways to acquire big data is to first examine your internal databases and then explore industry norms and standards. Your data analyst will sort, gather, structure, and present this information to help you gain a competitive advantage in your industry, as well as provide you with a solid starting point for any projects or operational changes on your calendar.

Not sure where to start or if you need a data analyst? Read our quick post, How to Use Data Analyst for Your Business to get started.

4 Types of Data to Transform Your Marketing

Developing a business strategy is no major ordeal. Yet creating one that effectively infers relevant data to enhance the businesses operations and sales is not something every data analyst in Washington D.C. is capable of. Knowing how to accumulate information is one thing, while realizing what to do with that information to change your business is a by and large a more diverse story. 

To utilize and structure data successfully it is vital to employ the right data analysts within your business and organization. Relevant information on a very basic level changes the way your organization contends and operates within your industry. Organizations that put resources into effectively analyzing data often gain esteem, confidence, and traction from this information. Leading analysts refer to digital data collecting ecosystems as the new structure of business. As industries continue to embrace and foster digital interactions with customers, the role of data continues to be challenge to keep up with, yet essential when protecting your competitive edge.

McKinsey & Company has noted the increasingly critical role that business analysts are playing in business today. A recent survey of 714 companies around the world revealed that ROI for investments into analytics pays off. The company explains, “Our findings paint a more nuanced picture of data analytics. When we evaluated its profitability and value-added productivity benefits, we found that they appear to be substantial—similar, in fact, to those experienced during earlier periods of intense IT investment. Our results indicated that to produce these significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities.”

Connecting Data and Marketing

Marketing data lies at the beginning of each fruitful marketing methodology. Data guides businesses and reveals various important starting points. The best data analysts in Washington, D.C. are capable of recommending who your best clients and prospects are, how to target them, how to build the right offers, and identify the right channels on which to present those offers. Additionally, your data analysts is experienced with recommending which messages drive the most changes, and how to improve customer retention.

As you work to achieve a specific marketing goal and develop your next marketing campaign, you initially need to completely comprehend who your clients and prospects are. This information and knowledge must go beyond basic demographics such as: names, addresses, telephone numbers, annual salaries, and email addresses. Customers and prospective clients anticipate that you know who they are, what they need, where to find them, and the best time to speak with them. To begin you must first gather relevant information and digest that information to aide in the launch on your next marketing campaign.   

Understanding how to use data in your next marketing campaign requires that you identify the most essential data needed prior to your launch. In general, this data is compiled by your data or business analyst in Washington, D.C. Through proper data analysis, planning, and implementation your next marketing campaign is much less likely to fail. Here are the 4 types of data you need when developing your marketing position.

Identifying Your Target Market

Begin by assembling factual data in regards to your target market such demographics, market fragment, their needs, and shopping preferences. Utilize your exploration to elucidate how to reach your prospective customers. Ask various questions to build a better profile of your customers such as age groups, gender, employment status, disposable income, and familial relations.

Also gather relevant information from your current business operations. Identifying the times of day your business or website is most profitable often sheds light on when your marketing campaigns should be launched. Taking into consideration the average transaction amounts and use of coupons or special offers provide insights into how your company is positioned within your industry.

Build a SWOT Analysis

The SWOT analysis is an acronym for strengths, weaknesses, opportunities, and threats for an organization or business. It was developed by Albert Humphrey during his tenure at Stanford University in 1960. His original goal was to identify why corporate planning failed. By embracing the SWOT analysis for your business you identify your competitor’s strengths in your industry, areas of improvement, and possible areas in which they fail. Knowing how to find this information takes some know-how. Your data analysts compiles this information to give you a solid approach on how to launch as well as what to expect. 

Gathering information on how your competition runs their marketing campaigns and positions their products is vital to developing successful marketing campaigns for our own brand. Consider various questions such as: what is their value proposition, how does your company differ and offer more, what do you like and what don't you like about their showcasing effort?

Price Your Products or Services Competitively

Very few organizations are able to set a price for their products or services without considering various costs such as shipping, manufacturing, and supplies. For service oriented companies these cost include operational influences including invoicing, ongoing training, and time. These cost directly affect the pricing of your competitors as well as your own. Gathering relevant information and insights into the cost of operations and current market trends provide you with the right data to build a successful and profitable pricing strategy. 

Research Your Marketplace

Whether you offer a service or product, it is critical to comprehend what is available to your customers at the time you plan to go to market. By gaining solid insights into the current offerings and practices of your competition you are able to identify the best ways to present your products as well as to whom. 

Researching your marketplace is often a combination of utilizing the information prepared in your SWOT analysis as well as your target market. As you learn more about your marketplace you are able to position and enhance your product or service in view of discoveries about what your prospective customers truly need and want. Concentrate on things such as capacity, appearance, online presences, and guarantees. 

Where is the best place to launch your product or introduce your service? Where would it be a good idea for you to disseminate from? Is a retail establishment the best stage for your item, or are your needs best met online?

Detailed and properly prepared market research data is one of the most important parts of any marketing strategy. The work of your data analyst in Washington, D.C. gives you a simple road map on how to position your company in the marketplace and thus avoid complete failure. Market research guarantees that your business understands your industry sector patterns, demographic moves, and adjustments needed as the economy shifts.

Data is a valuable asset in any sort of marketing and should never be overlooked. With consumers becoming more aware of the vast amount of offerings in the marketplace your statistics and data must be structured and compiled in a way that allows you to build a marketing campaign that engages, targets, and converts prospects. If not appropriately maintained and analyzed regularly by an experienced data analyst, you risk the chance of  diminished productivity, product launch failures, and lack of direction or purpose. 

Consult with an experienced data analyst from dc Analyst to learn more about how to utilize the information your company currently holds, as well as how to compile other data necessary to identify your target market and develop your pricing strategy.

9 Essential Skills Your Data Analyst Must Have

Data analysts are information translators that possess skill sets which greatly influence your business operations, sales goals, and various other projects. Through data analysis and understanding your business’s trends, patterns, and insights give you have the ability to simplify your sales funnel, reduce waste, and improve productivity. There are a few essential skills sets to look for when looking to hire a data analyst based in Washington, D.C.

In this article we touch on the most essential skills every data analysts needs in order to provide your business with insights and information that is relevant and conclusive. For information and detailed tips on how to use data analysts for your business read our short guide here. 

Organized and Detailed Work Ethic

When analyzing data even a small mistake can make a huge difference further along the line. If compiling, gathering, and segmenting information is performed in a haphazard way by your data analyst you could end up with misleading results and inconclusive solutions. Inaccurate information can point you in a false direction, waste time, and impact your ability to reach your goals.  

Given the importance of receiving and interpreting reliable data it is essential that your data analyst organize, structure, and present data in various forms. Prior to hiring your data analysts in Washington D.C. request to view spreadsheets, reports, and presentations. Check these items for details, structure, and relevant visuals.

Interpret A Database Query Language

It is often necessary to extract data in order to perform analysis operations. To do this well your data analyst should be experienced in a database query language such as SQL. This skill set ensures that the information is easily pulled upon request as well as analyzed properly. There are various forms of database query languages used today. 

Speak with you prospective data analysts about the languages they are well versed in. Some of the most popular softwares today include Apache Hive and Pig, and SparkSQL.  

Other statistical languages and packages that can be used by your analysts include SAS and SPSS. Being well versed in tools and languages ensures that your analyst is able to work effectively with minimal training and oversight. Additionally, having a command in various software languages and programming is a relevant skill for a data analyst who will be required to work with vast amounts of data frequently. Often these analyst hold certifications in select softwares and programming languages.

Proficient in Arithmetic

The saying goes, “Arithmetic is to mathematics as spelling is to writing.” Arithmetic is something we all learn from an early age, however, many of us stop at algebra or geometry. Your data analysts should be proficient in handling numbers and equations. Arithmetic is one’s ability to manipulate number sequences and often deals with calculations. Truly experienced data analyst are confident in calculations, sequencing, and numerical statements.  

As you seek a business and data analyst for your next project discuss the various forms of arithmetic they have engaged in from past 3 years. Choose the candidate with a strong background in arithmetic and experience in working with numbers on a regular basis. This skill set also simplifies how your reports are most likely to be presented and structured for easy understanding.  

Create User Friendly Spreadsheets

The ability to build and structure a spreadsheet is a basic requirement that is often overlooked.  It is vitally important for your data analyst to be able to take the information discovered and present it in a way that your entire team can understand and interpret. Spreadsheets with various headings, pivots, and charts are difficult to navigate for many users. The use of formulas, tables, and sorting should always be utilized to ensure your team is on one page. 

Your data analysts must be able to carrying out advanced tasks on a spreadsheet in order to manipulate the figures in numerous ways. It is not enough for a data analyst to just be able to put data into rows. Your project is likely to require a analysts that can complete tasks such as making reports and creating dashboards. 

Firm Grasp of Statistics

A data analyst must have a well-rounded knowledge of statistics. This allows them to determine when to use different techniques to achieve the required results. Identify a candidate with a sound knowledge in areas such as statistical tests, maximum likelihood estimators, and hypothesis testing. The ability to implement and utilize statistics for any project gives you sound results that are supported based on various factors such as annual data or industry trends. 

Ask your prospective data analyst how they plan to go about gathering statistics and using the information when preparing reports and summaries. Also consider asking them to research basic information and data about your industry and niche. Present this information to your team for review prior to beginning your project. 

Perform Data Wrangling or Munging

Data wrangling and mungling involves taking raw data and translating it into a form that is more conveniently consumed by semi-automated tools and programs. It is essential that your analyst possess this particular skill if you business has yet to implement a completely digital operating and sales process. Wrangling or munging involves finding a way of converting or mapping data that is presented in inconsistent formats. This makes raw data easier to work with and can save a lot of problems later on in the process.

Present your most challenging data scenario to your prospective analysts. Pay close attention to their ability to interpret, structure, and deliver the information in a more convenient form. 

Data Visualization Techniques

This essential skill simply answers the question: how will your data analyst present their findings to you? You need your business analyst to possess the skill set to present solutions in an easy to navigate and understand presentation. Visualization tools such as d3.js and ggplot are examples of the kind of visuals that you will want them to be familiar and well versed in. 

Flexible Communication Skills

At some point you will need to make big decisions based on the work carried out by your data analyst in Washington D.C. This means that you need an analyst who is excellent at communicating their findings with colleagues from different backgrounds, departments, and focuses. A good data analyst should be comfortable presenting presentations to both technical and non-technical members of your team. 

It is easier to evaluate communication skills in person. During your interview pay close attention to your data analyst’s ability to make eye contact, confidence, and speech patterns. Do they speak fast? Are they using language that is easy to understand?

Efficient Time Management

Hand holding for business owners is a nightmare. It is even more so when engaging the help of a data analyst. Selecting an analyst that has a proven track record of completing projects prior to due dates or within required time frames ensures that your data will always be present, up to date, and relevant as you require it. 

Efficiently managing time involves not simply just compiling and analyzing data but also presenting the data for your team to use when needed effectively. Ask your prospective analyst to provide examples of when they completed a project quickly with positive outcomes or samples of the presentation.  

Aside from a firm grasp on how to navigate business intelligence systems and analyzing information, your data analyst should also be proficient in these various other skills. An inexperienced data analyst may cause you to make mistakes in your everyday operations, delay launching a product, or reduce productivity. 

To find a data analyst that meets your needs and possesses these essential skills consult with dc Analyst to get started.