Data-driven decisions: Leveraging information advantages for corporate decision-making

By: Guest

29, May, 2019

Categories:

Artificial Intelligence - Business Intelligence - Data - IoT - Machine Learning -

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This article was contributed by Meltwater, a pioneer in Media Intelligence that gives businesses the information advantage they need to stay ahead. Meet them at stand 500!

It’s undeniable, artificial intelligence has become one of the most pervasive buzzwords in today’s tech scene. But AI has been a part of our daily lives for quite some time; from targeted advertising to autocorrect, it has made our day-to-day easier without us even thinking about it. In recent years, however, we’ve seen a huge uptick in interest, investment and implementation of AI-driven solutions. It’s not unreasonable to conclude that this ‘AI revolution’ will have wider and far-reaching implications as it’s technical capabilities grow and improve. In fact, experts predict that 70% of companies will have adopted AI by 2030.

While more companies are embracing AI, it’s still often seen as firmly belonging to the sphere of advanced technological innovation rather than a tool which is already used to facilitate better, more informed decision-making in all kinds of industries and business units. Here, we’ll dive into the process of using AI to create an information advantage and improve corporate decision-making.

Finding & Organizing data

One major reason AI has developed so quickly in recent years is down to increasing access to incredible amounts of data. To put things into perspective, 90% of all data that exists on the internet today was generated in the last 2 years. This equates to 2.5 quintillion bytes of data generated on a daily basis.  That’s billions and billions of individual data-points containing infinite amounts of information. We’re drowning in data and some companies are finding it difficult to stay afloat. The truth is, executives need insights, not data, and AI could be the key to translating data into actionable insights. Step one in this process is finding and organising data to fit corporate decision-making needs.

Internal data

Internal data refers to information generated, processed and stored within a company’s walls. This data (such as sales volumes, financial numbers, CRM data and so on) is analyzed internally and mostly form the basis of internal Business Intelligence efforts. This kind of data is vital to understanding your business successes and areas of improvement. The caveat is, this is historical data. Historical data enables us to look back at what we have done and often through the lens of our unavoidable internal biases.

External data

According to Meltwater’s CEO, Jorn Lyseggen, “External data offers us a real-time view of how our ecosystem and competitive landscape are evolving.” External data is information from the outside of a company – online breadcrumbs left by consumers, competitors, governments and anyone else who uses the world wide web. By leveraging the right tools, we have access to enormous amounts of this data, including online news sources, social media, job listings, legislation, reviews, patent filings and many other sources. When extracted correctly, outside data gives us the opportunity to take a 360° look at the context our business is operating in, allowing leaders to assess their own brand against the backdrop of the wider industry, in real-time. Such insights are then leveraged to make forward-looking decisions.

Broadly speaking, the data that’s out there can be divided into two categories, structured and unstructured. Structured data is computable, searchable and organised. It’s easy for machines to interpret and analyse this data, but the majority of the world’s data can’t actually be broken down into these neat data-sets without a bit of work first. In fact, the majority of data is unstructured. This doesn’t mean we can’t analyse it, on the contrary, unstructured data (like text, audio and video) is designed for human consumption. It is, however, difficult for algorithms to analyse as it doesn’t fit into neat boxes.

We can also add a third category to this: Alternative data, which refers to data that is ‘non-traditional’. Alternative data sources include everything from social media, to crowdfunding sites, corporate interviews, customer reviews, satellite imagery, geospatial information, hiring patterns and more. This type of data is growing in popularity amongst executives who are blending structured, unstructured and alternative data together to draw insights.

Enhancing data

In order for AI to make sense of alternative data, the data must first be enriched using advanced technology, for example, machine learning or natural language processing. According to Benedict Evans, Partner at Andreessen Horowitz, the use of machine learning powers three key possibilities:

Extracting insights

Once we’ve organised and processed what we need from the mountain of data that’s out there, it’s finally time to start making decisions. Here are a few ways executives are putting external and alternative data insights into practice.

Competitive intelligence

As the saying goes, “No man is an island” – the same applies for businesses. Your clients and prospects are being targeted every day by competitors and other organisations looking to engage the same audience. Luckily, whilst they’re doing this, they’re also leaving behind plenty of online breadcrumbs that you can track and analyse to anticipate their next move.

Common ways businesses are doing this include tracking conversation development around patent filings, R&D investments and ad-spend with the aim of predicting their competitor’s future focus and start listening to their customers to understand where the industry at large has room to improve.

Competitive intelligence is all about looking outside your company’s walls and understanding what’s going on in your industry. With this information, you can make better, more forward-thinking decisions for your organization based on a real-time understanding of your business context.

Benchmarking

Benchmarking involves comparing your own KPI performance to that of your competitors or with your own past performance. This allows you to contextualise your results and understand where you have or haven’t moved the needle. For example, if your sales are steadily growing, but your competitors are growing faster, that’s an important insight to know. But more crucially, you want to understand why. Connecting the dots is critical as it offers an action plan and focus point. For example, if you notice your competitor’s sales are growing and so is the number of patents they’re submitting, R&D job positions opening and media buzz around their new products, you can drill down deeper to figure out whether or not that’s the correlation. As a result, you may decide to focus on innovations in order to get ahead of the competition.

Predictive insights

Perhaps one of the most promising areas in which AI helps us to make better decisions is predictive insights. Using historical and real-time data, specially trained algorithms can look for patterns and signals which indicate what could happen in the future and alert you to real-time trends as they grow.

Using external data, we can predict potential IPOs, growth patterns, investments and more. Extracting these trends can give you the edge you need to prepare for disruptions in the market, seize opportunities as they arise, and mitigate risks by solving business problems before they hit.

If any business is to remain competitive in this brave new data-driven world, it’s essential to embrace technological advancements in AI creatively and implement them in a way that makes sense. Businesses who understand the potential and make an effort to extract insights from external data will give themselves an information advantage in forming strategies for the days, months and years to come.

Want to learn more about leveraging information advantages for corporate decision making? Come by stand number 500. Let’s chat!