By Marc Fuentes – Adlib
Is unstructured data running your business to the ground?
Most organizations today are falling victim to data chaos born out of the continuous growth of unstructured data. Information has become locked up in interdepartmental legacy systems, and the problem builds every day as data silos continue to get bigger.
As a result of the unstructured nature of this data, your business becomes vulnerable to cyberattacks as hackers today turn their attention to these weak points.
Additionally, this data becomes unhelpful for sales forecasting as it’s hard to track down what you need. All in all, the build-up of unstructured data hinders business productivity in crucial ways. The solution lies in creating a solid data strategy and incorporating the help of a powerful Content Intelligence Cloud to structure all your data. In this article, we’ll be discussing how to change your current data chaos trajectory to grow your business with a 5-step data strategy.
1. Define Data Goals
Every effective plan starts with a clear vision fueling its agenda. A concise definition of what your company aims to achieve is the launchpad to how to create a successful data strategy. But what is a data strategy in the first place? A data strategy refers to a comprehensive methodology for extracting, classifying, and managing a business’s existing data and its sources. Without data goals, it’ll be hard to identify data that’s valuable to your exact business needs. Instead, you’ll focus on an umbrella approach of structuring data and important information can slip through the cracks.
In addition to that, an absence of objectives means you won’t be able to effectively measure your progress. It’ll also be hard to determine the level of success of your data strategy, and what areas need improvement going forward.
To know what data you need and why start with the business as opposed to straight away beginning the digitization of data.
For example, if your endgame is Data Privacy Compliance, then set goals in line with that need. Say you’re expanding your business to the UK, but now you have to think about GDPR (EU data laws). Specifically, you’d perhaps like to minimize the amount of personally identifiable information you collect to comply with GDPR’s policy on data minimization, which states businesses shouldn’t collect more PII than they really need.
In this case, your goals could be based on the number of customer profiles you’d want to process in a day. You’ll also need to establish a clear timeframe for getting these tasks done.
2. Assemble Your Team
With objectives on paper, now comes the next step which is putting together a data strategy team. Unstructured data is a huge problem today, with experts estimating that between 80% to 90% of an organization’s data is unstructured. This is according to an IBM report.
If that’s the case for your business, and you find it hard to even trace financial reports going back a year, then you’ll need all hands on deck to better comb through the data rubble. With unstructured data drowning your company, it can be too much work to go at it alone. So you’ll need some extra pairs of hands to not only help with data collection but also identify data points you might miss.
Therefore, when assembling your team, you’ll want employees at the forefront of critical data processes involved, e.g. accounting clerks, claims processing, customer service staff, logistics/supply chain staff, etc. That way, it’ll be easier to identify how and where data silos are nesting. Additionally, these front-line workers are ideal because they have first-hand experience with data challenges.
The key to mastering how to build a data strategy lies in not only recruiting a team but also making everyone’s responsibilities clear from the get-go. For example, you could have your CFO in charge of identifying data challenges and silos in the accounting department. Other managers can do the same for their areas of oversight. Although you can accomplish your goals with in-house talent, you might also want to think about adding a data engineer or analyst to your ranks.
3. Find Technology Partners
Unstructured data sources can span thousands of data points. Even with the largest manpower in the world, it’ll still be hard to identify all the dark data buried within the deepest reaches of your legacy systems.
It comes in all different forms, including text in files and emails, audio and video, that cannot be stored in a traditional column-based database. That is to say that storage costs can escalate real quick, not to mention the initial costs of acquiring storage hardware, backup facilities, and expert data security personnel.
Add that to the fact that more than 80% of enterprise data is unstructured, and that this data is growing at a rate of 55% and 65% annually.
A content intelligence platform is the best foundation for a reliable data strategy built for the long haul.
It is an indispensable part of figuring out how to change your current data chaos trajectory to grow your business with the help of AI. More specifically, you want content intelligence software that includes cloud storage because, while unstructured data is a valuable source of business intelligence, it is a potential storage headache.
Adlib software uses AI-driven OCR Software optical character recognition to extract data from over 600 formats, including MS Office documents, images, and legacy content. We then store your data securely in the cloud, immediately cutting down storage costs that you’d have had to shoulder.
Via machine learning algorithms, Adlib further identifies and categorizes documents for better searchability. Our platform then offers cloud storage to ensure this data is available to the various branches of your organization.
4. Migrate Your Data
After assembling a data team and choosing your technology partner comes the heart of the work: data migration. What is data migration? Crucially, I can tell you what it’s not first.
The transfer of content from one data repository to another is only one aspect of the process; it is not all there is to effective data migration.
Strategic data migration is a crucial aspect of mastering how to create a data strategy that works, and encompasses digitization, data transference, and data cleaning. For more insight on creating a successful data migration strategy, this article offers a 6-step breakdown of the process.
Advisably, you can split up the data migration project into departments with short-term goals specific to those areas.
An HR manager, for instance, can oversee migration in his area, and work with HR officers to provide a compilation of customer PII that they interact with at that stage of the workflow cycle. This information should compile both structured and unstructured data sources used on a day-to-day basis.
Once various team officials have handed in their findings, it’s time to work out how much content you want to transfer and the various file formats involved. While you’re at it, this is the time to identify duplicate or generally unhelpful content that will unnecessarily inflate your efforts or expenses, and clean those up. With content intelligence software, you can automate most of these data migration processes by setting a few sorting rules and some button clicks.
5. Implement Data Governance
With your data structured, there’s the need to ensure that it stays that way. Probably your business found itself in the middle of data chaos due to the absence of a formal data governance strategy, which is a problem affecting 44% of American companies as per a Rand Secure data survey.
The first step involves defining a data hierarchy and determining who will be in charge of what data governance responsibilities going forward. For example, at the top of that chain, you could have a Chief Data Officer (CDO) who, in collaboration with your board, could create data policies and management strategies. Below the CDO, data owners oversee the work of data stewards, acting as senior-level managers who report to the chief data officer. At the bottom of the hierarchy, data stewards enforce these policies across the organization to ensure adherence to a proper data governance strategy.
Stewards can also monitor data collection in their various domains to ensure quality and eliminate redundancies and duplications. Front-desk officers in various departments can double up as data stewards, although it’s best to appoint dedicated personnel for these roles.
Moreover, part of your data governance strategy should involve planning for data storage as well.
An Intelligent Document Processing partner worth their salt will often suggest the cloud to avoid interdepartmental obscurity of data.
Conclusion
Is unstructured data holding your business back?
Maybe you’re having a tough time tracking down confidential customer information for redaction and compliance needs. Perhaps, your customer service processes take too long as manual data collection stagnates the process. Demand forecasting may also be a little off as dark data continues to hide crucial financial insights and trends from your business.
Whatever problem you’re facing due to unstructured data, a data strategy holds the key to unlocking how to change your current data chaos trajectory to grow your business.
Creating a data strategy and seeing it through all the way is a lot of work, so you’ll be best served with an intelligent document processing software, which automates most of the processes involved.
The longer you wait the bigger the unstructured data gets, and the challenges that come with it. So it’s best to get your data strategy up and running right away.
About the Author – Marc Fuentes
Marc is the Senior Director of Growth Marketing at Adlib. For 20+ years, Marc has been leading marketing and sales teams within service, software, and SaaS. During his career, he has delivered over $30M+ in revenue and launched 65+ products for some of North America’s leading brands. In his off time, Marc enjoys staying active and spending time with his wife and three kids.